args : add -kvu to llama-parallel

Fix the seg fault without NCCL

.devops/nix/nixpkgs-instances.nix

@@ -4,7 +4,7 @@
   # the module `{ pkgs ... }: { /* config */ }` implicitly uses
   # `_module.args.pkgs` (defined in this case by flake-parts).
   perSystem =
-    { system, ... }:
+    { lib, system, ... }:
     {
       _module.args = {
         # Note: bringing up https://zimbatm.com/notes/1000-instances-of-nixpkgs
@@ -33,7 +33,7 @@
                 "CUDA EULA"
                 "cuDNN EULA"
               ]
-            ) (p.meta.licenses or [ p.meta.license ]);
+            ) (p.meta.licenses or (lib.toList p.meta.license));
         };
         # Ensure dependencies use ROCm consistently
         pkgsRocm = import inputs.nixpkgs {

.devops/nix/package-gguf-py.nix

@@ -3,6 +3,7 @@
   llamaVersion,
   numpy,
   tqdm,
+  requests,
   sentencepiece,
   pyyaml,
   poetry-core,
@@ -20,6 +21,7 @@ buildPythonPackage {
     tqdm
     sentencepiece
     pyyaml
+    requests
   ];
   src = lib.cleanSource ../../gguf-py;
   pythonImportsCheck = [

.devops/nix/scope.nix

@@ -7,13 +7,6 @@
 
 let
   pythonPackages = python3.pkgs;
-  buildPythonPackage = pythonPackages.buildPythonPackage;
-  numpy = pythonPackages.numpy;
-  tqdm = pythonPackages.tqdm;
-  sentencepiece = pythonPackages.sentencepiece;
-  pyyaml = pythonPackages.pyyaml;
-  poetry-core = pythonPackages.poetry-core;
-  pytestCheckHook = pythonPackages.pytestCheckHook;
 in
 
 # We're using `makeScope` instead of just writing out an attrset
@@ -23,17 +16,18 @@ in
 lib.makeScope newScope (self: {
   inherit llamaVersion;
   gguf-py = self.callPackage ./package-gguf-py.nix {
-    inherit
-      buildPythonPackage
+    inherit (pythonPackages)
       numpy
       tqdm
       sentencepiece
-      poetry-core
       pyyaml
       pytestCheckHook
+      requests
+      buildPythonPackage
+      poetry-core
       ;
   };
-  python-scripts = self.callPackage ./python-scripts.nix { inherit buildPythonPackage poetry-core; };
+  python-scripts = self.callPackage ./python-scripts.nix { inherit (pythonPackages) buildPythonPackage poetry-core; };
   llama-cpp = self.callPackage ./package.nix { };
   docker = self.callPackage ./docker.nix { };
   docker-min = self.callPackage ./docker.nix { interactive = false; };

.devops/vulkan.Dockerfile

@@ -54,6 +54,7 @@ RUN apt-get update \
     build-essential \
     git \
     python3 \
+    python3-dev \
     python3-pip \
     python3-wheel \
     && pip install --break-system-packages --upgrade setuptools \

.github/workflows/build.yml

@@ -21,7 +21,8 @@ on:
       '**/*.m',
       '**/*.metal',
       '**/*.comp',
-      '**/*.glsl'
+      '**/*.glsl',
+      '**/*.wgsl'
     ]
 
   pull_request:
@@ -42,7 +43,8 @@ on:
       '**/*.m',
       '**/*.metal',
       '**/*.comp',
-      '**/*.glsl'
+      '**/*.glsl',
+      '**/*.wgsl'
     ]
 
 concurrency:
@@ -291,7 +293,9 @@ jobs:
           cmake -B build \
             -DLLAMA_FATAL_WARNINGS=ON \
             -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON \
+            -DGGML_SANITIZE_${{ matrix.sanitizer }}=ON \
             -DCMAKE_BUILD_TYPE=${{ matrix.build_type }}
+
           cmake --build build --config ${{ matrix.build_type }} -j $(nproc)
 
       - name: Build (no OpenMP)
@@ -301,8 +305,10 @@ jobs:
           cmake -B build \
             -DLLAMA_FATAL_WARNINGS=ON \
             -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON \
+            -DGGML_SANITIZE_${{ matrix.sanitizer }}=ON \
             -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \
             -DGGML_OPENMP=OFF
+
           cmake --build build --config ${{ matrix.build_type }} -j $(nproc)
 
       - name: Test
@@ -464,7 +470,7 @@ jobs:
           export GGML_VK_VISIBLE_DEVICES=0
           export GGML_VK_DISABLE_F16=1
           # This is using llvmpipe and runs slower than other backends
-          ctest -L main --verbose --timeout 4200
+          ctest -L main --verbose --timeout 4800
 
   ubuntu-24-cmake-webgpu:
     runs-on: ubuntu-24.04
@@ -1371,7 +1377,7 @@ jobs:
         id: update_presets
         if: ${{ matrix.build == 'arm64-snapdragon' }}
         run: |
-          cp docs/backend/hexagon/CMakeUserPresets.json .
+          cp docs/backend/snapdragon/CMakeUserPresets.json .
 
       - name: Build
         id: ndk_build
@@ -1530,7 +1536,7 @@ jobs:
       - name: Test
         id: ggml-ci
         run: |
-          LLAMA_ARG_THREADS=$(nproc) bash ./ci/run.sh ./tmp/results ./tmp/mnt
+          LLAMA_ARG_THREADS=$(nproc) GG_BUILD_HIGH_PERF=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt
 
   ggml-ci-arm64-cpu-high-perf:
     runs-on: ubuntu-22.04-arm
@@ -1556,7 +1562,7 @@ jobs:
       - name: Test
         id: ggml-ci
         run: |
-          LLAMA_ARG_THREADS=$(nproc) GG_BUILD_NO_SVE=1 GG_BUILD_NO_BF16=1 GG_BUILD_EXTRA_TESTS_0=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt
+          LLAMA_ARG_THREADS=$(nproc) GG_BUILD_HIGH_PERF=1 GG_BUILD_NO_SVE=1 GG_BUILD_NO_BF16=1 GG_BUILD_EXTRA_TESTS_0=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt
 
   ggml-ci-arm64-cpu-high-perf-sve:
     runs-on: ubuntu-22.04-arm

.github/workflows/server-metal.yml

@@ -0,0 +1,73 @@
+name: Server-Metal
+
+on:
+  workflow_dispatch: # allows manual triggering
+    inputs:
+      sha:
+        description: 'Commit SHA1 to build'
+        required: false
+        type: string
+      slow_tests:
+        description: 'Run slow tests'
+        required: true
+        type: boolean
+  push:
+    branches:
+      - master
+    paths: ['.github/workflows/server-metal.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'tools/server/**.*']
+
+env:
+  LLAMA_LOG_COLORS: 1
+  LLAMA_LOG_PREFIX: 1
+  LLAMA_LOG_TIMESTAMPS: 1
+  LLAMA_LOG_VERBOSITY: 10
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}-${{ github.head_ref || github.run_id }}
+  cancel-in-progress: true
+
+jobs:
+  server-metal:
+    runs-on: [self-hosted, macOS, ARM64]
+
+    name: server-metal (${{ matrix.wf_name }})
+    strategy:
+      matrix:
+        build_type: [Release]
+        wf_name: ["GPUx1"]
+        include:
+          - build_type: Release
+            extra_args: "LLAMA_ARG_BACKEND_SAMPLING=1"
+            wf_name:    "GPUx1, backend-sampling"
+          - build_type: Release
+            extra_args: "GGML_METAL_DEVICES=2"
+            wf_name:    "GPUx2"
+          - build_type: Release
+            extra_args: "GGML_METAL_DEVICES=2 LLAMA_ARG_BACKEND_SAMPLING=1"
+            wf_name:    "GPUx2, backend-sampling"
+      fail-fast: false
+
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v6
+        with:
+          fetch-depth: 0
+          ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}
+
+      - name: Build
+        id: cmake_build
+        run: |
+          cmake -B build -DGGML_SCHED_NO_REALLOC=ON
+          cmake --build build --config ${{ matrix.build_type }} -j $(sysctl -n hw.logicalcpu) --target llama-server
+
+      - name: Tests
+        id: server_integration_tests
+        if: ${{ (!matrix.disabled_on_pr || !github.event.pull_request) }}
+        run: |
+          cd tools/server/tests
+          python3 -m venv venv
+          source venv/bin/activate
+          pip install -r requirements.txt
+          export ${{ matrix.extra_args }}
+          pytest -v -x -m "not slow"

.github/workflows/server-webui.yml

@@ -8,10 +8,6 @@ on:
         description: 'Commit SHA1 to build'
         required: false
         type: string
-      slow_tests:
-        description: 'Run slow tests'
-        required: true
-        type: boolean
   push:
     branches:
       - master
@@ -101,119 +97,3 @@ jobs:
         if: ${{ always() && steps.playwright.conclusion == 'success' }}
         run: npm run test:e2e
         working-directory: tools/server/webui
-
-  server-build:
-    runs-on: ubuntu-latest
-
-    strategy:
-      matrix:
-        sanitizer: [ADDRESS, UNDEFINED] # THREAD is broken
-        build_type: [RelWithDebInfo]
-        include:
-          - build_type: Release
-            sanitizer: ""
-      fail-fast: false # While -DLLAMA_SANITIZE_THREAD=ON is broken
-
-    steps:
-      - name: Dependencies
-        id: depends
-        run: |
-          sudo apt-get update
-          sudo apt-get -y install \
-            build-essential \
-            xxd \
-            git \
-            cmake \
-            curl \
-            wget \
-            language-pack-en \
-            libssl-dev
-
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v6
-        with:
-          fetch-depth: 0
-          ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}
-
-      - name: Python setup
-        id: setup_python
-        uses: actions/setup-python@v6
-        with:
-          python-version: '3.11'
-
-      - name: Tests dependencies
-        id: test_dependencies
-        run: |
-          pip install -r tools/server/tests/requirements.txt
-
-      - name: Setup Node.js for WebUI
-        uses: actions/setup-node@v6
-        with:
-          node-version: "22"
-          cache: "npm"
-          cache-dependency-path: "tools/server/webui/package-lock.json"
-
-      - name: Install WebUI dependencies
-        run: npm ci
-        working-directory: tools/server/webui
-
-      - name: Build WebUI
-        run: npm run build
-        working-directory: tools/server/webui
-
-      - name: Build (no OpenMP)
-        id: cmake_build_no_openmp
-        if: ${{ matrix.sanitizer == 'THREAD' }}
-        run: |
-          cmake -B build \
-              -DGGML_NATIVE=OFF \
-              -DLLAMA_BUILD_SERVER=ON \
-              -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \
-              -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON \
-              -DGGML_OPENMP=OFF ;
-          cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target llama-server
-
-      - name: Build (sanitizers)
-        id: cmake_build_sanitizers
-        if: ${{ matrix.sanitizer != '' && matrix.sanitizer != 'THREAD' }}
-        run: |
-          cmake -B build \
-              -DGGML_NATIVE=OFF \
-              -DLLAMA_BUILD_SERVER=ON \
-              -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \
-              -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON ;
-          cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target llama-server
-
-      - name: Build (sanitizers)
-        id: cmake_build
-        if: ${{ matrix.sanitizer == '' }}
-        run: |
-          cmake -B build \
-              -DGGML_NATIVE=OFF \
-              -DLLAMA_BUILD_SERVER=ON \
-              -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} ;
-          cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target llama-server
-
-      - name: Tests
-        id: server_integration_tests
-        if: ${{ matrix.sanitizer == '' }}
-        env:
-          GITHUB_ACTIONS: "true"
-        run: |
-          cd tools/server/tests
-          ./tests.sh
-
-      - name: Tests (sanitizers)
-        id: server_integration_tests_sanitizers
-        if: ${{ matrix.sanitizer != '' }}
-        run: |
-          cd tools/server/tests
-          LLAMA_SANITIZE=1 ./tests.sh
-
-      - name: Slow tests
-        id: server_integration_tests_slow
-        if: ${{ (github.event.schedule || github.event.inputs.slow_tests == 'true') && matrix.build_type == 'Release' }}
-        run: |
-          cd tools/server/tests
-          SLOW_TESTS=1 ./tests.sh

.github/workflows/server.yml

@@ -36,7 +36,7 @@ jobs:
 
     strategy:
       matrix:
-        sanitizer: [ADDRESS, UNDEFINED] # THREAD is broken
+        sanitizer: [ADDRESS, UNDEFINED] # THREAD is very slow
         build_type: [RelWithDebInfo]
         include:
           - build_type: Release
@@ -45,7 +45,7 @@ jobs:
           - build_type: Release
             sanitizer: ""
             extra_args: "LLAMA_ARG_BACKEND_SAMPLING=1"
-      fail-fast: false # While -DLLAMA_SANITIZE_THREAD=ON is broken
+      fail-fast: false
 
     steps:
       - name: Dependencies
@@ -72,28 +72,40 @@ jobs:
       - name: Build
         id: cmake_build
         run: |
-          cmake -B build -DLLAMA_BUILD_BORINGSSL=ON -DGGML_SCHED_NO_REALLOC=ON
-          cmake --build build --config ${{ matrix.build_type }} -j ${env:NUMBER_OF_PROCESSORS} --target llama-server
+          cmake -B build \
+            -DLLAMA_BUILD_BORINGSSL=ON \
+            -DGGML_SCHED_NO_REALLOC=ON \
+            -DGGML_SANITIZE_ADDRESS=${{ matrix.sanitizer == 'ADDRESS' }} \
+            -DGGML_SANITIZE_THREAD=${{ matrix.sanitizer == 'THREAD' }} \
+            -DGGML_SANITIZE_UNDEFINED=${{ matrix.sanitizer == 'UNDEFINED' }} \
+            -DLLAMA_SANITIZE_ADDRESS=${{ matrix.sanitizer == 'ADDRESS' }} \
+            -DLLAMA_SANITIZE_THREAD=${{ matrix.sanitizer == 'THREAD' }} \
+            -DLLAMA_SANITIZE_UNDEFINED=${{ matrix.sanitizer == 'UNDEFINED' }}
+          cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target llama-server
 
       - name: Python setup
         id: setup_python
         uses: actions/setup-python@v6
         with:
           python-version: '3.11'
-
-      - name: Tests dependencies
-        id: test_dependencies
-        run: |
-          pip install -r tools/server/tests/requirements.txt
+          pip-install: -r tools/server/tests/requirements.txt
 
       - name: Tests
         id: server_integration_tests
-        if: ${{ (!matrix.disabled_on_pr || !github.event.pull_request) && matrix.build_type == 'Release' }}
+        if: ${{ (!matrix.disabled_on_pr || !github.event.pull_request) }}
         run: |
           cd tools/server/tests
           export ${{ matrix.extra_args }}
           pytest -v -x -m "not slow"
 
+      - name: Slow tests
+        id: server_integration_tests_slow
+        if: ${{ (github.event.schedule || github.event.inputs.slow_tests == 'true') && matrix.build_type == 'Release' }}
+        run: |
+          cd tools/server/tests
+          export ${{ matrix.extra_args }}
+          SLOW_TESTS=1 pytest -v -x
+
   server-windows:
     runs-on: windows-2022
 
@@ -116,11 +128,7 @@ jobs:
         uses: actions/setup-python@v6
         with:
           python-version: '3.11'
-
-      - name: Tests dependencies
-        id: test_dependencies
-        run: |
-          pip install -r tools/server/tests/requirements.txt
+          pip-install: -r tools/server/tests/requirements.txt
 
       - name: Tests
         id: server_integration_tests

.github/workflows/winget.yml

@@ -28,16 +28,17 @@ jobs:
               owner: context.repo.owner,
               repo: context.repo.repo,
             });
-            console.log("Latest release:", releases[0].tag_name);
-            return releases[0].tag_name;
+            const { tag_name: version, assets: assets } = releases.find(({assets}) => assets.find(asset => asset.name.includes('win-vulkan')));
+            const { browser_download_url: asset_url } = assets.find(asset => asset.name.includes('win-vulkan'));
+            console.log("Latest release:", version);
+            core.setOutput('VERSION', version);
+            core.setOutput('ASSETURL', asset_url);
 
       - name: Update manifest
-        env:
-          VERSION: ${{ steps.find_latest_release.outputs.result }}
         run: |
           echo "Updating manifest..."
-          komac update --version ${{ env.VERSION }} \
-            --urls "https://github.com/ggml-org/llama.cpp/releases/download/${{ env.VERSION }}/llama-${{ env.VERSION }}-bin-win-vulkan-x64.zip" \
+          komac update --version ${{ steps.find_latest_release.outputs.VERSION }} \
+            --urls "${{ steps.find_latest_release.outputs.ASSETURL }}" \
             --token ${{ secrets.WINGET_GITHUB_TOKEN }} \
             --submit \
             ggml.llamacpp

CMakeLists.txt

@@ -109,6 +109,7 @@ option(LLAMA_BUILD_TOOLS    "llama: build tools"          ${LLAMA_STANDALONE})
 option(LLAMA_BUILD_EXAMPLES "llama: build examples"       ${LLAMA_STANDALONE})
 option(LLAMA_BUILD_SERVER   "llama: build server example" ${LLAMA_STANDALONE})
 option(LLAMA_TOOLS_INSTALL  "llama: install tools"        ${LLAMA_TOOLS_INSTALL_DEFAULT})
+option(LLAMA_TESTS_INSTALL  "llama: install tests"        ON)
 
 # 3rd party libs
 option(LLAMA_HTTPLIB    "llama: httplib for downloading functionality" ON)
@@ -164,29 +165,6 @@ llama_option_depr(WARNING     LLAMA_SYCL                GGML_SYCL)
 llama_option_depr(WARNING     LLAMA_SYCL_F16            GGML_SYCL_F16)
 llama_option_depr(WARNING     LLAMA_CANN                GGML_CANN)
 
-if (NOT MSVC)
-    if (LLAMA_SANITIZE_THREAD)
-        message(STATUS "Using -fsanitize=thread")
-
-        add_compile_options(-fsanitize=thread)
-        link_libraries     (-fsanitize=thread)
-    endif()
-
-    if (LLAMA_SANITIZE_ADDRESS)
-        message(STATUS "Using -fsanitize=address")
-
-        add_compile_options(-fsanitize=address -fno-omit-frame-pointer)
-        link_libraries     (-fsanitize=address)
-    endif()
-
-    if (LLAMA_SANITIZE_UNDEFINED)
-        message(STATUS "Using -fsanitize=undefined")
-
-        add_compile_options(-fsanitize=undefined)
-        link_libraries     (-fsanitize=undefined)
-    endif()
-endif()
-
 include("cmake/license.cmake")
 license_add_file("llama.cpp" "LICENSE")
 

CODEOWNERS

@@ -18,6 +18,7 @@
 /common/jinja/                          @ngxson @CISC @aldehir
 /common/llguidance.*                    @ggerganov
 /common/log.*                           @ggerganov
+/common/ngram-map.*                     @srogmann
 /common/peg-parser.*                    @aldehir
 /common/sampling.*                      @ggerganov
 /common/speculative.*                   @ggerganov
@@ -26,6 +27,7 @@
 /examples/batched.swift/                @ggerganov
 /examples/batched/                      @ggerganov
 /examples/convert-llama2c-to-ggml/      @ggerganov
+/examples/debug/                        @danbev @pwilkin
 /examples/deprecation-warning/          @ggerganov
 /examples/diffusion/                    @am17an
 /examples/embedding/                    @ggerganov

CONTRIBUTING.md

@@ -20,7 +20,7 @@ If AI is used to generate any portion of the code, contributors must adhere to t
 1. Explicitly disclose the manner in which AI was employed.
 2. Perform a comprehensive manual review prior to submitting the pull request.
 3. Be prepared to explain every line of code they submitted when asked about it by a maintainer.
-4. Using AI to write pull request descriptions or to respond to human reviewers is strictly prohibited.
+4. It is strictly prohibited to use AI to write your posts for you (bug reports, feature requests, pull request descriptions, Github discussions, responding to humans, ...).
 
 For more info, please refer to the [AGENTS.md](AGENTS.md) file.
 

LICENSE

@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2023-2024 The ggml authors
+Copyright (c) 2023-2026 The ggml authors
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

README.md

@@ -213,6 +213,7 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
 - [llama.vim](https://github.com/ggml-org/llama.vim) (MIT)
 - [LARS](https://github.com/abgulati/LARS) (AGPL)
 - [Llama Assistant](https://github.com/vietanhdev/llama-assistant) (GPL)
+- [LlamaLib](https://github.com/undreamai/LlamaLib) (Apache-2.0)
 - [LLMFarm](https://github.com/guinmoon/LLMFarm?tab=readme-ov-file) (MIT)
 - [LLMUnity](https://github.com/undreamai/LLMUnity) (MIT)
 - [LMStudio](https://lmstudio.ai/) (proprietary)
@@ -287,6 +288,7 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
 | [WebGPU [In Progress]](docs/build.md#webgpu) | All |
 | [RPC](https://github.com/ggml-org/llama.cpp/tree/master/tools/rpc) | All |
 | [Hexagon [In Progress]](docs/backend/hexagon/README.md) | Snapdragon |
+| [VirtGPU](docs/backend/VirtGPU.md) | VirtGPU APIR |
 
 ## Obtaining and quantizing models
 

benches/dgx-spark/dgx-spark.md

@@ -8,7 +8,7 @@ g++ --version
 g++ (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0
 
 nvidia-smi
-Sun Nov  2 10:43:25 2025
+Thu Feb  5 13:49:40 2026
 +-----------------------------------------------------------------------------------------+
 | NVIDIA-SMI 580.95.05              Driver Version: 580.95.05      CUDA Version: 13.0     |
 +-----------------------------------------+------------------------+----------------------+
@@ -17,7 +17,7 @@ Sun Nov  2 10:43:25 2025
 |                                         |                        |               MIG M. |
 |=========================================+========================+======================|
 |   0  NVIDIA GB10                    On  |   0000000F:01:00.0 Off |                  N/A |
-| N/A   35C    P8              4W /  N/A  | Not Supported          |      0%      Default |
+| N/A   47C    P0             13W /  N/A  | Not Supported          |      0%      Default |
 |                                         |                        |                  N/A |
 +-----------------------------------------+------------------------+----------------------+
 ```
@@ -29,46 +29,46 @@ Model: https://huggingface.co/ggml-org/gpt-oss-20b-GGUF
 - `llama-batched-bench`
 
 
-main: n_kv_max = 270336, n_batch = 2048, n_ubatch = 2048, flash_attn = 1, is_pp_shared = 0, n_gpu_layers = -1, n_threads = 20, n_threads_batch = 20
+main: n_kv_max = 270336, n_batch = 2048, n_ubatch = 2048, flash_attn = 1, is_pp_shared = 0, is_tg_separate = 0, n_gpu_layers = -1, n_threads = 20, n_threads_batch = 20
 
 |    PP |     TG |    B |   N_KV |   T_PP s | S_PP t/s |   T_TG s | S_TG t/s |      T s |    S t/s |
 |-------|--------|------|--------|----------|----------|----------|----------|----------|----------|
-|   512 |     32 |    1 |    544 |    0.374 |  1369.01 |    0.383 |    83.64 |    0.757 |   719.01 |
-|   512 |     32 |    2 |   1088 |    0.274 |  3741.35 |    0.659 |    97.14 |    0.933 |  1166.66 |
-|   512 |     32 |    4 |   2176 |    0.526 |  3896.47 |    0.817 |   156.73 |    1.342 |  1621.08 |
-|   512 |     32 |    8 |   4352 |    1.044 |  3925.10 |    0.987 |   259.44 |    2.030 |  2143.56 |
-|   512 |     32 |   16 |   8704 |    2.076 |  3945.84 |    1.248 |   410.32 |    3.324 |  2618.60 |
-|   512 |     32 |   32 |  17408 |    4.170 |  3929.28 |    1.630 |   628.40 |    5.799 |  3001.76 |
-|  4096 |     32 |    1 |   4128 |    1.083 |  3782.66 |    0.394 |    81.21 |    1.477 |  2795.13 |
-|  4096 |     32 |    2 |   8256 |    2.166 |  3782.72 |    0.725 |    88.28 |    2.891 |  2856.14 |
-|  4096 |     32 |    4 |  16512 |    4.333 |  3780.88 |    0.896 |   142.82 |    5.230 |  3157.38 |
-|  4096 |     32 |    8 |  33024 |    8.618 |  3802.14 |    1.155 |   221.69 |    9.773 |  3379.08 |
-|  4096 |     32 |   16 |  66048 |   17.330 |  3781.73 |    1.598 |   320.34 |   18.928 |  3489.45 |
-|  4096 |     32 |   32 | 132096 |   34.671 |  3780.48 |    2.336 |   438.35 |   37.007 |  3569.51 |
-|  8192 |     32 |    1 |   8224 |    2.233 |  3668.56 |    0.438 |    72.98 |    2.671 |  3078.44 |
-|  8192 |     32 |    2 |  16448 |    4.425 |  3702.95 |    0.756 |    84.66 |    5.181 |  3174.95 |
-|  8192 |     32 |    4 |  32896 |    8.859 |  3698.64 |    0.967 |   132.38 |    9.826 |  3347.72 |
-|  8192 |     32 |    8 |  65792 |   17.714 |  3699.57 |    1.277 |   200.52 |   18.991 |  3464.35 |
-|  8192 |     32 |   16 | 131584 |   35.494 |  3692.84 |    1.841 |   278.12 |   37.335 |  3524.46 |
-|  8192 |     32 |   32 | 263168 |   70.949 |  3694.82 |    2.798 |   365.99 |   73.747 |  3568.53 |
+|   512 |     32 |    1 |    544 |    0.270 |  1895.57 |    0.399 |    80.13 |    0.669 |   812.60 |
+|   512 |     32 |    2 |   1088 |    0.230 |  4451.23 |    0.583 |   109.71 |    0.813 |  1337.56 |
+|   512 |     32 |    4 |   2176 |    0.437 |  4688.87 |    0.820 |   156.03 |    1.257 |  1730.91 |
+|   512 |     32 |    8 |   4352 |    0.863 |  4744.23 |    0.942 |   271.79 |    1.805 |  2410.73 |
+|   512 |     32 |   16 |   8704 |    1.725 |  4748.19 |    1.173 |   436.38 |    2.899 |  3002.85 |
+|   512 |     32 |   32 |  17408 |    3.437 |  4767.38 |    1.503 |   681.49 |    4.939 |  3524.40 |
+|  4096 |     32 |    1 |   4128 |    0.907 |  4513.91 |    0.407 |    78.54 |    1.315 |  3139.56 |
+|  4096 |     32 |    2 |   8256 |    1.796 |  4560.42 |    0.625 |   102.37 |    2.422 |  3409.45 |
+|  4096 |     32 |    4 |  16512 |    3.596 |  4555.66 |    0.888 |   144.11 |    4.485 |  3681.93 |
+|  4096 |     32 |    8 |  33024 |    7.184 |  4561.44 |    1.098 |   233.11 |    8.282 |  3987.51 |
+|  4096 |     32 |   16 |  66048 |   14.369 |  4560.82 |    1.503 |   340.74 |   15.872 |  4161.30 |
+|  4096 |     32 |   32 | 132096 |   28.760 |  4557.52 |    2.162 |   473.59 |   30.922 |  4271.95 |
+|  8192 |     32 |    1 |   8224 |    1.859 |  4405.59 |    0.430 |    74.36 |    2.290 |  3591.61 |
+|  8192 |     32 |    2 |  16448 |    3.698 |  4430.02 |    0.656 |    97.59 |    4.354 |  3777.47 |
+|  8192 |     32 |    4 |  32896 |    7.403 |  4426.10 |    0.957 |   133.82 |    8.360 |  3934.97 |
+|  8192 |     32 |    8 |  65792 |   14.802 |  4427.63 |    1.222 |   209.44 |   16.024 |  4105.87 |
+|  8192 |     32 |   16 | 131584 |   29.596 |  4428.67 |    1.741 |   294.13 |   31.337 |  4199.00 |
+|  8192 |     32 |   32 | 263168 |   59.169 |  4430.42 |    2.619 |   390.92 |   61.789 |  4259.17 |
 
 
 - `llama-bench`
 
-| model                          |       size |     params | backend    | ngl | n_ubatch | fa | mmap |            test |                  t/s |
-| ------------------------------ | ---------: | ---------: | ---------- | --: | -------: | -: | ---: | --------------: | -------------------: |
-| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | CUDA       |  99 |     2048 |  1 |    0 |          pp2048 |      3714.25 ± 20.36 |
-| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | CUDA       |  99 |     2048 |  1 |    0 |            tg32 |         86.58 ± 0.43 |
-| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | CUDA       |  99 |     2048 |  1 |    0 |  pp2048 @ d4096 |      3445.17 ± 17.85 |
-| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | CUDA       |  99 |     2048 |  1 |    0 |    tg32 @ d4096 |         81.72 ± 0.53 |
-| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | CUDA       |  99 |     2048 |  1 |    0 |  pp2048 @ d8192 |      3218.78 ± 11.34 |
-| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | CUDA       |  99 |     2048 |  1 |    0 |    tg32 @ d8192 |         74.86 ± 0.64 |
-| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | CUDA       |  99 |     2048 |  1 |    0 | pp2048 @ d16384 |       2732.83 ± 7.17 |
-| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | CUDA       |  99 |     2048 |  1 |    0 |   tg32 @ d16384 |         71.57 ± 0.51 |
-| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | CUDA       |  99 |     2048 |  1 |    0 | pp2048 @ d32768 |      2119.75 ± 12.81 |
-| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | CUDA       |  99 |     2048 |  1 |    0 |   tg32 @ d32768 |         62.33 ± 0.24 |
+| model                          |       size |     params | backend    | ngl | n_ubatch | fa | mmap | dio |            test |                  t/s |
+| ------------------------------ | ---------: | ---------: | ---------- | --: | -------: | -: | ---: | --: | --------------: | -------------------: |
+| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |          pp2048 |      4505.82 ± 12.90 |
+| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |            tg32 |         83.43 ± 0.59 |
+| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |  pp2048 @ d4096 |      4158.34 ± 18.84 |
+| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |    tg32 @ d4096 |         79.22 ± 0.60 |
+| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |  pp2048 @ d8192 |      3993.81 ± 17.55 |
+| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |    tg32 @ d8192 |         75.22 ± 1.05 |
+| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | CUDA       |  99 |     2048 |  1 |    0 |   1 | pp2048 @ d16384 |      3449.98 ± 12.13 |
+| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |   tg32 @ d16384 |         70.36 ± 0.37 |
+| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | CUDA       |  99 |     2048 |  1 |    0 |   1 | pp2048 @ d32768 |      2689.42 ± 18.89 |
+| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |   tg32 @ d32768 |         61.65 ± 0.30 |
 
-build: eeee367de (6989)
+build: 11fb327bf (7941)
 
 ## ggml-org/gpt-oss-120b-GGUF
 
@@ -77,46 +77,46 @@ Model: https://huggingface.co/ggml-org/gpt-oss-120b-GGUF
 - `llama-batched-bench`
 
 
-main: n_kv_max = 270336, n_batch = 2048, n_ubatch = 2048, flash_attn = 1, is_pp_shared = 0, n_gpu_layers = -1, n_threads = 20, n_threads_batch = 20
+main: n_kv_max = 270336, n_batch = 2048, n_ubatch = 2048, flash_attn = 1, is_pp_shared = 0, is_tg_separate = 0, n_gpu_layers = -1, n_threads = 20, n_threads_batch = 20
 
 |    PP |     TG |    B |   N_KV |   T_PP s | S_PP t/s |   T_TG s | S_TG t/s |      T s |    S t/s |
 |-------|--------|------|--------|----------|----------|----------|----------|----------|----------|
-|   512 |     32 |    1 |    544 |    0.571 |   897.18 |    0.543 |    58.96 |    1.113 |   488.60 |
-|   512 |     32 |    2 |   1088 |    0.593 |  1725.37 |    1.041 |    61.45 |    1.635 |   665.48 |
-|   512 |     32 |    4 |   2176 |    1.043 |  1963.15 |    1.334 |    95.95 |    2.377 |   915.36 |
-|   512 |     32 |    8 |   4352 |    2.099 |  1951.63 |    1.717 |   149.07 |    3.816 |  1140.45 |
-|   512 |     32 |   16 |   8704 |    4.207 |  1947.12 |    2.311 |   221.56 |    6.518 |  1335.35 |
-|   512 |     32 |   32 |  17408 |    8.422 |  1945.36 |    3.298 |   310.46 |   11.720 |  1485.27 |
-|  4096 |     32 |    1 |   4128 |    2.138 |  1915.88 |    0.571 |    56.09 |    2.708 |  1524.12 |
-|  4096 |     32 |    2 |   8256 |    4.266 |  1920.25 |    1.137 |    56.27 |    5.404 |  1527.90 |
-|  4096 |     32 |    4 |  16512 |    8.564 |  1913.02 |    1.471 |    86.99 |   10.036 |  1645.29 |
-|  4096 |     32 |    8 |  33024 |   17.092 |  1917.19 |    1.979 |   129.33 |   19.071 |  1731.63 |
-|  4096 |     32 |   16 |  66048 |   34.211 |  1915.65 |    2.850 |   179.66 |   37.061 |  1782.15 |
-|  4096 |     32 |   32 | 132096 |   68.394 |  1916.44 |    4.381 |   233.72 |   72.775 |  1815.13 |
-|  8192 |     32 |    1 |   8224 |    4.349 |  1883.45 |    0.620 |    51.65 |    4.969 |  1655.04 |
-|  8192 |     32 |    2 |  16448 |    8.674 |  1888.83 |    1.178 |    54.33 |    9.852 |  1669.48 |
-|  8192 |     32 |    4 |  32896 |   17.351 |  1888.55 |    1.580 |    81.01 |   18.931 |  1737.68 |
-|  8192 |     32 |    8 |  65792 |   34.743 |  1886.31 |    2.173 |   117.80 |   36.916 |  1782.20 |
-|  8192 |     32 |   16 | 131584 |   69.413 |  1888.29 |    3.297 |   155.28 |   72.710 |  1809.70 |
-|  8192 |     32 |   32 | 263168 |  138.903 |  1887.24 |    5.004 |   204.63 |  143.907 |  1828.73 |
+|   512 |     32 |    1 |    544 |    0.445 |  1151.80 |    0.560 |    57.14 |    1.005 |   541.53 |
+|   512 |     32 |    2 |   1088 |    0.472 |  2169.85 |    0.874 |    73.27 |    1.345 |   808.65 |
+|   512 |     32 |    4 |   2176 |    0.826 |  2480.33 |    1.299 |    98.51 |    2.125 |  1023.94 |
+|   512 |     32 |    8 |   4352 |    1.644 |  2491.67 |    1.608 |   159.18 |    3.252 |  1338.20 |
+|   512 |     32 |   16 |   8704 |    3.292 |  2488.35 |    2.117 |   241.85 |    5.409 |  1609.13 |
+|   512 |     32 |   32 |  17408 |    6.604 |  2481.07 |    2.898 |   353.31 |    9.502 |  1832.04 |
+|  4096 |     32 |    1 |   4128 |    1.698 |  2412.65 |    0.580 |    55.21 |    2.277 |  1812.66 |
+|  4096 |     32 |    2 |   8256 |    3.399 |  2409.88 |    0.934 |    68.53 |    4.333 |  1905.27 |
+|  4096 |     32 |    4 |  16512 |    6.823 |  2401.21 |    1.411 |    90.72 |    8.234 |  2005.30 |
+|  4096 |     32 |    8 |  33024 |   13.574 |  2413.97 |    1.841 |   139.07 |   15.415 |  2142.31 |
+|  4096 |     32 |   16 |  66048 |   27.176 |  2411.52 |    2.609 |   196.26 |   29.785 |  2217.49 |
+|  4096 |     32 |   32 | 132096 |   54.359 |  2411.23 |    3.905 |   262.20 |   58.264 |  2267.19 |
+|  8192 |     32 |    1 |   8224 |    3.491 |  2346.81 |    0.613 |    52.23 |    4.103 |  2004.21 |
+|  8192 |     32 |    2 |  16448 |    6.939 |  2361.03 |    0.981 |    65.21 |    7.921 |  2076.56 |
+|  8192 |     32 |    4 |  32896 |   13.888 |  2359.40 |    1.511 |    84.71 |   15.399 |  2136.21 |
+|  8192 |     32 |    8 |  65792 |   27.756 |  2361.18 |    2.034 |   125.86 |   29.790 |  2208.56 |
+|  8192 |     32 |   16 | 131584 |   55.554 |  2359.34 |    3.021 |   169.49 |   58.575 |  2246.41 |
+|  8192 |     32 |   32 | 263168 |  111.036 |  2360.89 |    4.537 |   225.72 |  115.573 |  2277.08 |
 
 
 - `llama-bench`
 
-| model                          |       size |     params | backend    | ngl | n_ubatch | fa | mmap |            test |                  t/s |
-| ------------------------------ | ---------: | ---------: | ---------- | --: | -------: | -: | ---: | --------------: | -------------------: |
-| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | CUDA       |  99 |     2048 |  1 |    0 |          pp2048 |       1919.36 ± 5.01 |
-| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | CUDA       |  99 |     2048 |  1 |    0 |            tg32 |         60.40 ± 0.30 |
-| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | CUDA       |  99 |     2048 |  1 |    0 |  pp2048 @ d4096 |       1825.30 ± 6.37 |
-| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | CUDA       |  99 |     2048 |  1 |    0 |    tg32 @ d4096 |         56.94 ± 0.29 |
-| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | CUDA       |  99 |     2048 |  1 |    0 |  pp2048 @ d8192 |       1739.19 ± 6.00 |
-| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | CUDA       |  99 |     2048 |  1 |    0 |    tg32 @ d8192 |         52.51 ± 0.42 |
-| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | CUDA       |  99 |     2048 |  1 |    0 | pp2048 @ d16384 |       1536.75 ± 4.27 |
-| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | CUDA       |  99 |     2048 |  1 |    0 |   tg32 @ d16384 |         49.33 ± 0.27 |
-| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | CUDA       |  99 |     2048 |  1 |    0 | pp2048 @ d32768 |       1255.85 ± 3.26 |
-| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | CUDA       |  99 |     2048 |  1 |    0 |   tg32 @ d32768 |         42.99 ± 0.18 |
+| model                          |       size |     params | backend    | ngl | n_ubatch | fa | mmap | dio |            test |                  t/s |
+| ------------------------------ | ---------: | ---------: | ---------- | --: | -------: | -: | ---: | --: | --------------: | -------------------: |
+| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |          pp2048 |       2443.91 ± 7.47 |
+| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |            tg32 |         58.72 ± 0.20 |
+| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |  pp2048 @ d4096 |       2309.84 ± 3.63 |
+| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |    tg32 @ d4096 |         55.67 ± 0.35 |
+| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |  pp2048 @ d8192 |      2216.68 ± 10.16 |
+| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |    tg32 @ d8192 |         52.87 ± 0.43 |
+| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | CUDA       |  99 |     2048 |  1 |    0 |   1 | pp2048 @ d16384 |       1956.31 ± 6.39 |
+| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |   tg32 @ d16384 |         49.45 ± 0.20 |
+| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | CUDA       |  99 |     2048 |  1 |    0 |   1 | pp2048 @ d32768 |      1567.08 ± 11.79 |
+| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |   tg32 @ d32768 |         42.76 ± 0.14 |
 
-build: eeee367de (6989)
+build: 11fb327bf (7941)
 
 ## ggml-org/Qwen3-Coder-30B-A3B-Instruct-Q8_0-GGUF
 
@@ -125,46 +125,46 @@ Model: https://huggingface.co/ggml-org/Qwen3-Coder-30B-A3B-Instruct-Q8_0-GGUF
 - `llama-batched-bench`
 
 
-main: n_kv_max = 270336, n_batch = 2048, n_ubatch = 2048, flash_attn = 1, is_pp_shared = 0, n_gpu_layers = -1, n_threads = 20, n_threads_batch = 20
+main: n_kv_max = 270336, n_batch = 2048, n_ubatch = 2048, flash_attn = 1, is_pp_shared = 0, is_tg_separate = 0, n_gpu_layers = -1, n_threads = 20, n_threads_batch = 20
 
 |    PP |     TG |    B |   N_KV |   T_PP s | S_PP t/s |   T_TG s | S_TG t/s |      T s |    S t/s |
 |-------|--------|------|--------|----------|----------|----------|----------|----------|----------|
-|   512 |     32 |    1 |    544 |    0.398 |  1285.90 |    0.530 |    60.41 |    0.928 |   586.27 |
-|   512 |     32 |    2 |   1088 |    0.386 |  2651.65 |    0.948 |    67.50 |    1.334 |   815.38 |
-|   512 |     32 |    4 |   2176 |    0.666 |  3076.37 |    1.209 |   105.87 |    1.875 |  1160.71 |
-|   512 |     32 |    8 |   4352 |    1.325 |  3091.39 |    1.610 |   158.98 |    2.935 |  1482.65 |
-|   512 |     32 |   16 |   8704 |    2.664 |  3075.58 |    2.150 |   238.19 |    4.813 |  1808.39 |
-|   512 |     32 |   32 |  17408 |    5.336 |  3070.31 |    2.904 |   352.59 |    8.240 |  2112.50 |
-|  4096 |     32 |    1 |   4128 |    1.444 |  2836.81 |    0.581 |    55.09 |    2.025 |  2038.81 |
-|  4096 |     32 |    2 |   8256 |    2.872 |  2852.14 |    1.084 |    59.06 |    3.956 |  2086.99 |
-|  4096 |     32 |    4 |  16512 |    5.744 |  2852.32 |    1.440 |    88.90 |    7.184 |  2298.47 |
-|  4096 |     32 |    8 |  33024 |   11.463 |  2858.68 |    2.068 |   123.78 |   13.531 |  2440.65 |
-|  4096 |     32 |   16 |  66048 |   22.915 |  2859.95 |    3.018 |   169.67 |   25.933 |  2546.90 |
-|  4096 |     32 |   32 | 132096 |   45.956 |  2852.10 |    4.609 |   222.18 |   50.565 |  2612.39 |
-|  8192 |     32 |    1 |   8224 |    3.063 |  2674.72 |    0.693 |    46.20 |    3.755 |  2189.92 |
-|  8192 |     32 |    2 |  16448 |    6.109 |  2681.87 |    1.214 |    52.71 |    7.323 |  2245.98 |
-|  8192 |     32 |    4 |  32896 |   12.197 |  2686.63 |    1.682 |    76.11 |   13.878 |  2370.30 |
-|  8192 |     32 |    8 |  65792 |   24.409 |  2684.94 |    2.556 |   100.17 |   26.965 |  2439.95 |
-|  8192 |     32 |   16 | 131584 |   48.753 |  2688.50 |    3.994 |   128.20 |   52.747 |  2494.64 |
-|  8192 |     32 |   32 | 263168 |   97.508 |  2688.42 |    6.528 |   156.86 |  104.037 |  2529.57 |
+|   512 |     32 |    1 |    544 |    0.393 |  1303.73 |    0.548 |    58.36 |    0.941 |   578.10 |
+|   512 |     32 |    2 |   1088 |    0.387 |  2648.68 |    0.910 |    70.35 |    1.296 |   839.27 |
+|   512 |     32 |    4 |   2176 |    0.659 |  3107.63 |    1.302 |    98.33 |    1.961 |  1109.77 |
+|   512 |     32 |    8 |   4352 |    1.322 |  3099.35 |    1.669 |   153.42 |    2.990 |  1455.43 |
+|   512 |     32 |   16 |   8704 |    2.639 |  3104.63 |    2.212 |   231.44 |    4.851 |  1794.32 |
+|   512 |     32 |   32 |  17408 |    5.284 |  3100.80 |    2.955 |   346.53 |    8.239 |  2112.93 |
+|  4096 |     32 |    1 |   4128 |    1.417 |  2890.36 |    0.598 |    53.51 |    2.015 |  2048.45 |
+|  4096 |     32 |    2 |   8256 |    2.829 |  2895.62 |    1.019 |    62.82 |    3.848 |  2145.60 |
+|  4096 |     32 |    4 |  16512 |    5.656 |  2896.96 |    1.528 |    83.79 |    7.183 |  2298.71 |
+|  4096 |     32 |    8 |  33024 |   11.338 |  2890.02 |    2.127 |   120.36 |   13.465 |  2452.53 |
+|  4096 |     32 |   16 |  66048 |   22.709 |  2885.96 |    3.104 |   164.97 |   25.812 |  2558.79 |
+|  4096 |     32 |   32 | 132096 |   45.301 |  2893.35 |    4.723 |   216.80 |   50.024 |  2640.63 |
+|  8192 |     32 |    1 |   8224 |    3.022 |  2711.09 |    0.678 |    47.20 |    3.700 |  2222.89 |
+|  8192 |     32 |    2 |  16448 |    6.039 |  2713.01 |    1.149 |    55.70 |    7.188 |  2288.21 |
+|  8192 |     32 |    4 |  32896 |   12.050 |  2719.35 |    1.785 |    71.69 |   13.835 |  2377.67 |
+|  8192 |     32 |    8 |  65792 |   24.113 |  2717.90 |    2.629 |    97.39 |   26.741 |  2460.31 |
+|  8192 |     32 |   16 | 131584 |   48.178 |  2720.58 |    4.099 |   124.91 |   52.277 |  2517.06 |
+|  8192 |     32 |   32 | 263168 |   96.401 |  2719.31 |    6.696 |   152.93 |  103.097 |  2552.63 |
 
 
 - `llama-bench`
 
-| model                          |       size |     params | backend    | ngl | n_ubatch | fa | mmap |            test |                  t/s |
-| ------------------------------ | ---------: | ---------: | ---------- | --: | -------: | -: | ---: | --------------: | -------------------: |
-| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | CUDA       |  99 |     2048 |  1 |    0 |          pp2048 |       2925.55 ± 4.25 |
-| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | CUDA       |  99 |     2048 |  1 |    0 |            tg32 |         62.80 ± 0.27 |
-| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | CUDA       |  99 |     2048 |  1 |    0 |  pp2048 @ d4096 |       2531.01 ± 6.79 |
-| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | CUDA       |  99 |     2048 |  1 |    0 |    tg32 @ d4096 |         55.86 ± 0.33 |
-| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | CUDA       |  99 |     2048 |  1 |    0 |  pp2048 @ d8192 |       2244.39 ± 5.33 |
-| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | CUDA       |  99 |     2048 |  1 |    0 |    tg32 @ d8192 |         45.95 ± 0.33 |
-| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | CUDA       |  99 |     2048 |  1 |    0 | pp2048 @ d16384 |       1783.17 ± 3.68 |
-| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | CUDA       |  99 |     2048 |  1 |    0 |   tg32 @ d16384 |         39.07 ± 0.10 |
-| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | CUDA       |  99 |     2048 |  1 |    0 | pp2048 @ d32768 |       1241.90 ± 3.13 |
-| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | CUDA       |  99 |     2048 |  1 |    0 |   tg32 @ d32768 |         29.92 ± 0.06 |
+| model                          |       size |     params | backend    | ngl | n_ubatch | fa | mmap | dio |            test |                  t/s |
+| ------------------------------ | ---------: | ---------: | ---------- | --: | -------: | -: | ---: | --: | --------------: | -------------------: |
+| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |          pp2048 |      2986.97 ± 18.87 |
+| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |            tg32 |         61.06 ± 0.23 |
+| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |  pp2048 @ d4096 |       2633.45 ± 6.26 |
+| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |    tg32 @ d4096 |         54.77 ± 0.28 |
+| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |  pp2048 @ d8192 |       2354.14 ± 3.84 |
+| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |    tg32 @ d8192 |         48.02 ± 0.40 |
+| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | CUDA       |  99 |     2048 |  1 |    0 |   1 | pp2048 @ d16384 |       1908.86 ± 4.25 |
+| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |   tg32 @ d16384 |         40.23 ± 0.10 |
+| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | CUDA       |  99 |     2048 |  1 |    0 |   1 | pp2048 @ d32768 |       1348.17 ± 2.00 |
+| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |   tg32 @ d32768 |         30.21 ± 0.04 |
 
-build: eeee367de (6989)
+build: 11fb327bf (7941)
 
 ## ggml-org/Qwen2.5-Coder-7B-Q8_0-GGUF
 
@@ -173,46 +173,46 @@ Model: https://huggingface.co/ggml-org/Qwen2.5-Coder-7B-Q8_0-GGUF
 - `llama-batched-bench`
 
 
-main: n_kv_max = 270336, n_batch = 2048, n_ubatch = 2048, flash_attn = 1, is_pp_shared = 0, n_gpu_layers = -1, n_threads = 20, n_threads_batch = 20
+main: n_kv_max = 270336, n_batch = 2048, n_ubatch = 2048, flash_attn = 1, is_pp_shared = 0, is_tg_separate = 0, n_gpu_layers = -1, n_threads = 20, n_threads_batch = 20
 
 |    PP |     TG |    B |   N_KV |   T_PP s | S_PP t/s |   T_TG s | S_TG t/s |      T s |    S t/s |
 |-------|--------|------|--------|----------|----------|----------|----------|----------|----------|
-|   512 |     32 |    1 |    544 |    0.211 |  2421.57 |    1.055 |    30.33 |    1.266 |   429.57 |
-|   512 |     32 |    2 |   1088 |    0.419 |  2441.34 |    1.130 |    56.65 |    1.549 |   702.32 |
-|   512 |     32 |    4 |   2176 |    0.873 |  2345.54 |    1.174 |   108.99 |    2.048 |  1062.74 |
-|   512 |     32 |    8 |   4352 |    1.727 |  2371.85 |    1.254 |   204.22 |    2.980 |  1460.19 |
-|   512 |     32 |   16 |   8704 |    3.452 |  2373.22 |    1.492 |   343.16 |    4.944 |  1760.56 |
-|   512 |     32 |   32 |  17408 |    6.916 |  2368.93 |    1.675 |   611.51 |    8.591 |  2026.36 |
-|  4096 |     32 |    1 |   4128 |    1.799 |  2277.26 |    1.084 |    29.51 |    2.883 |  1431.91 |
-|  4096 |     32 |    2 |   8256 |    3.577 |  2290.01 |    1.196 |    53.50 |    4.774 |  1729.51 |
-|  4096 |     32 |    4 |  16512 |    7.172 |  2284.36 |    1.313 |    97.50 |    8.485 |  1946.00 |
-|  4096 |     32 |    8 |  33024 |   14.341 |  2284.96 |    1.520 |   168.46 |   15.860 |  2082.18 |
-|  4096 |     32 |   16 |  66048 |   28.675 |  2285.44 |    1.983 |   258.21 |   30.658 |  2154.33 |
-|  4096 |     32 |   32 | 132096 |   57.354 |  2285.32 |    2.640 |   387.87 |   59.994 |  2201.82 |
-|  8192 |     32 |    1 |   8224 |    3.701 |  2213.75 |    1.119 |    28.59 |    4.820 |  1706.34 |
-|  8192 |     32 |    2 |  16448 |    7.410 |  2211.19 |    1.272 |    50.31 |    8.682 |  1894.56 |
-|  8192 |     32 |    4 |  32896 |   14.802 |  2213.83 |    1.460 |    87.68 |   16.261 |  2022.96 |
-|  8192 |     32 |    8 |  65792 |   29.609 |  2213.35 |    1.781 |   143.74 |   31.390 |  2095.93 |
-|  8192 |     32 |   16 | 131584 |   59.229 |  2212.96 |    2.495 |   205.17 |   61.725 |  2131.79 |
-|  8192 |     32 |   32 | 263168 |  118.449 |  2213.15 |    3.714 |   275.75 |  122.162 |  2154.25 |
+|   512 |     32 |    1 |    544 |    0.212 |  2420.12 |    1.100 |    29.10 |    1.311 |   414.85 |
+|   512 |     32 |    2 |   1088 |    0.428 |  2393.89 |    1.185 |    54.00 |    1.613 |   674.56 |
+|   512 |     32 |    4 |   2176 |    0.894 |  2290.41 |    1.229 |   104.17 |    2.123 |  1025.02 |
+|   512 |     32 |    8 |   4352 |    1.758 |  2330.36 |    1.319 |   194.15 |    3.076 |  1414.70 |
+|   512 |     32 |   16 |   8704 |    3.508 |  2335.21 |    1.543 |   331.90 |    5.051 |  1723.33 |
+|   512 |     32 |   32 |  17408 |    7.035 |  2328.93 |    1.738 |   589.21 |    8.773 |  1984.29 |
+|  4096 |     32 |    1 |   4128 |    1.831 |  2237.25 |    1.125 |    28.44 |    2.956 |  1396.42 |
+|  4096 |     32 |    2 |   8256 |    3.642 |  2249.48 |    1.253 |    51.07 |    4.895 |  1686.64 |
+|  4096 |     32 |    4 |  16512 |    7.274 |  2252.26 |    1.380 |    92.72 |    8.655 |  1907.81 |
+|  4096 |     32 |    8 |  33024 |   14.576 |  2248.09 |    1.617 |   158.29 |   16.193 |  2039.37 |
+|  4096 |     32 |   16 |  66048 |   29.138 |  2249.17 |    2.081 |   246.01 |   31.219 |  2115.63 |
+|  4096 |     32 |   32 | 132096 |   58.275 |  2249.19 |    2.814 |   363.87 |   61.089 |  2162.34 |
+|  8192 |     32 |    1 |   8224 |    3.757 |  2180.26 |    1.184 |    27.03 |    4.941 |  1664.37 |
+|  8192 |     32 |    2 |  16448 |    7.522 |  2178.05 |    1.341 |    47.73 |    8.863 |  1855.77 |
+|  8192 |     32 |    4 |  32896 |   15.043 |  2178.25 |    1.548 |    82.69 |   16.591 |  1982.74 |
+|  8192 |     32 |    8 |  65792 |   30.111 |  2176.49 |    1.937 |   132.13 |   32.048 |  2052.90 |
+|  8192 |     32 |   16 | 131584 |   60.405 |  2169.90 |    2.706 |   189.21 |   63.111 |  2084.97 |
+|  8192 |     32 |   32 | 263168 |  120.439 |  2176.58 |    3.993 |   256.46 |  124.432 |  2114.96 |
 
 
 - `llama-bench`
 
-| model                          |       size |     params | backend    | ngl | n_ubatch | fa | mmap |            test |                  t/s |
-| ------------------------------ | ---------: | ---------: | ---------- | --: | -------: | -: | ---: | --------------: | -------------------: |
-| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | CUDA       |  99 |     2048 |  1 |    0 |          pp2048 |       2272.74 ± 4.68 |
-| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | CUDA       |  99 |     2048 |  1 |    0 |            tg32 |         30.66 ± 0.02 |
-| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | CUDA       |  99 |     2048 |  1 |    0 |  pp2048 @ d4096 |       2107.80 ± 9.55 |
-| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | CUDA       |  99 |     2048 |  1 |    0 |    tg32 @ d4096 |         29.71 ± 0.05 |
-| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | CUDA       |  99 |     2048 |  1 |    0 |  pp2048 @ d8192 |       1937.80 ± 6.75 |
-| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | CUDA       |  99 |     2048 |  1 |    0 |    tg32 @ d8192 |         28.86 ± 0.04 |
-| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | CUDA       |  99 |     2048 |  1 |    0 | pp2048 @ d16384 |       1641.12 ± 1.78 |
-| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | CUDA       |  99 |     2048 |  1 |    0 |   tg32 @ d16384 |         27.24 ± 0.04 |
-| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | CUDA       |  99 |     2048 |  1 |    0 | pp2048 @ d32768 |       1296.02 ± 2.67 |
-| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | CUDA       |  99 |     2048 |  1 |    0 |   tg32 @ d32768 |         23.78 ± 0.03 |
+| model                          |       size |     params | backend    | ngl | n_ubatch | fa | mmap | dio |            test |                  t/s |
+| ------------------------------ | ---------: | ---------: | ---------- | --: | -------: | -: | ---: | --: | --------------: | -------------------: |
+| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |          pp2048 |       2250.28 ± 6.41 |
+| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |            tg32 |         29.43 ± 0.02 |
+| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |  pp2048 @ d4096 |       2100.19 ± 8.96 |
+| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |    tg32 @ d4096 |         28.61 ± 0.02 |
+| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |  pp2048 @ d8192 |       2007.56 ± 4.16 |
+| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |    tg32 @ d8192 |         27.38 ± 0.09 |
+| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | CUDA       |  99 |     2048 |  1 |    0 |   1 | pp2048 @ d16384 |       1779.11 ± 6.42 |
+| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |   tg32 @ d16384 |         25.72 ± 0.03 |
+| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | CUDA       |  99 |     2048 |  1 |    0 |   1 | pp2048 @ d32768 |       1471.23 ± 1.71 |
+| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |   tg32 @ d32768 |         22.51 ± 0.02 |
 
-build: eeee367de (6989)
+build: 11fb327bf (7941)
 
 ## ggml-org/gemma-3-4b-it-qat-GGUF
 
@@ -221,44 +221,91 @@ Model: https://huggingface.co/ggml-org/gemma-3-4b-it-qat-GGUF
 - `llama-batched-bench`
 
 
-main: n_kv_max = 270336, n_batch = 2048, n_ubatch = 2048, flash_attn = 1, is_pp_shared = 0, n_gpu_layers = -1, n_threads = 20, n_threads_batch = 20
+main: n_kv_max = 270336, n_batch = 2048, n_ubatch = 2048, flash_attn = 1, is_pp_shared = 0, is_tg_separate = 0, n_gpu_layers = -1, n_threads = 20, n_threads_batch = 20
 
 |    PP |     TG |    B |   N_KV |   T_PP s | S_PP t/s |   T_TG s | S_TG t/s |      T s |    S t/s |
 |-------|--------|------|--------|----------|----------|----------|----------|----------|----------|
-|   512 |     32 |    1 |    544 |    0.094 |  5434.73 |    0.394 |    81.21 |    0.488 |  1114.15 |
-|   512 |     32 |    2 |   1088 |    0.168 |  6091.68 |    0.498 |   128.52 |    0.666 |  1633.41 |
-|   512 |     32 |    4 |   2176 |    0.341 |  6010.68 |    0.542 |   236.37 |    0.882 |  2466.43 |
-|   512 |     32 |    8 |   4352 |    0.665 |  6161.46 |    0.678 |   377.74 |    1.342 |  3241.72 |
-|   512 |     32 |   16 |   8704 |    1.323 |  6193.19 |    0.902 |   567.41 |    2.225 |  3911.74 |
-|   512 |     32 |   32 |  17408 |    2.642 |  6202.03 |    1.231 |   832.03 |    3.872 |  4495.36 |
-|  4096 |     32 |    1 |   4128 |    0.701 |  5840.49 |    0.439 |    72.95 |    1.140 |  3621.23 |
-|  4096 |     32 |    2 |   8256 |    1.387 |  5906.82 |    0.574 |   111.48 |    1.961 |  4210.12 |
-|  4096 |     32 |    4 |  16512 |    2.758 |  5940.33 |    0.651 |   196.58 |    3.409 |  4843.33 |
-|  4096 |     32 |    8 |  33024 |    5.491 |  5967.56 |    0.876 |   292.40 |    6.367 |  5187.12 |
-|  4096 |     32 |   16 |  66048 |   10.978 |  5969.58 |    1.275 |   401.69 |   12.253 |  5390.38 |
-|  4096 |     32 |   32 | 132096 |   21.944 |  5972.93 |    1.992 |   514.16 |   23.936 |  5518.73 |
-|  8192 |     32 |    1 |   8224 |    1.402 |  5841.91 |    0.452 |    70.73 |    1.855 |  4434.12 |
-|  8192 |     32 |    2 |  16448 |    2.793 |  5865.34 |    0.637 |   100.55 |    3.430 |  4795.51 |
-|  8192 |     32 |    4 |  32896 |    5.564 |  5889.64 |    0.770 |   166.26 |    6.334 |  5193.95 |
-|  8192 |     32 |    8 |  65792 |   11.114 |  5896.44 |    1.122 |   228.07 |   12.237 |  5376.51 |
-|  8192 |     32 |   16 | 131584 |   22.210 |  5901.38 |    1.789 |   286.15 |   24.000 |  5482.74 |
-|  8192 |     32 |   32 | 263168 |   44.382 |  5906.56 |    3.044 |   336.38 |   47.426 |  5549.02 |
+|   512 |     32 |    1 |    544 |    0.092 |  5566.97 |    0.412 |    77.63 |    0.504 |  1078.95 |
+|   512 |     32 |    2 |   1088 |    0.161 |  6345.67 |    0.522 |   122.70 |    0.683 |  1593.06 |
+|   512 |     32 |    4 |   2176 |    0.325 |  6309.87 |    0.562 |   227.68 |    0.887 |  2453.87 |
+|   512 |     32 |    8 |   4352 |    0.643 |  6374.42 |    0.685 |   373.67 |    1.328 |  3277.94 |
+|   512 |     32 |   16 |   8704 |    1.277 |  6413.64 |    0.915 |   559.47 |    2.192 |  3970.01 |
+|   512 |     32 |   32 |  17408 |    2.518 |  6506.57 |    1.249 |   819.61 |    3.767 |  4620.64 |
+|  4096 |     32 |    1 |   4128 |    0.674 |  6079.68 |    0.453 |    70.60 |    1.127 |  3662.88 |
+|  4096 |     32 |    2 |   8256 |    1.335 |  6137.82 |    0.627 |   102.03 |    1.962 |  4208.11 |
+|  4096 |     32 |    4 |  16512 |    2.657 |  6167.35 |    0.749 |   170.92 |    3.405 |  4848.71 |
+|  4096 |     32 |    8 |  33024 |    5.307 |  6173.91 |    0.974 |   262.89 |    6.281 |  5257.53 |
+|  4096 |     32 |   16 |  66048 |   10.610 |  6176.96 |    1.379 |   371.42 |   11.988 |  5509.40 |
+|  4096 |     32 |   32 | 132096 |   21.213 |  6178.89 |    2.122 |   482.50 |   23.335 |  5660.82 |
+|  8192 |     32 |    1 |   8224 |    1.359 |  6027.34 |    0.467 |    68.52 |    1.826 |  4503.48 |
+|  8192 |     32 |    2 |  16448 |    2.699 |  6069.68 |    0.653 |    98.03 |    3.352 |  4906.68 |
+|  8192 |     32 |    4 |  32896 |    5.366 |  6106.74 |    0.818 |   156.55 |    6.184 |  5319.96 |
+|  8192 |     32 |    8 |  65792 |   10.755 |  6093.50 |    1.174 |   218.04 |   11.929 |  5515.22 |
+|  8192 |     32 |   16 | 131584 |   21.484 |  6100.82 |    1.829 |   279.90 |   23.314 |  5644.11 |
+|  8192 |     32 |   32 | 263168 |   42.950 |  6103.40 |    3.058 |   334.91 |   46.008 |  5720.05 |
 
 
 - `llama-bench`
 
-| model                          |       size |     params | backend    | ngl | n_ubatch | fa | mmap |            test |                  t/s |
-| ------------------------------ | ---------: | ---------: | ---------- | --: | -------: | -: | ---: | --------------: | -------------------: |
-| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | CUDA       |  99 |     2048 |  1 |    0 |          pp2048 |      5810.04 ± 21.71 |
-| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | CUDA       |  99 |     2048 |  1 |    0 |            tg32 |         84.54 ± 0.18 |
-| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | CUDA       |  99 |     2048 |  1 |    0 |  pp2048 @ d4096 |       5288.04 ± 3.54 |
-| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | CUDA       |  99 |     2048 |  1 |    0 |    tg32 @ d4096 |         78.82 ± 1.37 |
-| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | CUDA       |  99 |     2048 |  1 |    0 |  pp2048 @ d8192 |      4960.43 ± 16.64 |
-| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | CUDA       |  99 |     2048 |  1 |    0 |    tg32 @ d8192 |         74.13 ± 0.30 |
-| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | CUDA       |  99 |     2048 |  1 |    0 | pp2048 @ d16384 |      4495.92 ± 31.11 |
-| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | CUDA       |  99 |     2048 |  1 |    0 |   tg32 @ d16384 |         72.37 ± 0.29 |
-| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | CUDA       |  99 |     2048 |  1 |    0 | pp2048 @ d32768 |      3746.90 ± 40.01 |
-| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | CUDA       |  99 |     2048 |  1 |    0 |   tg32 @ d32768 |         63.02 ± 0.20 |
+| model                          |       size |     params | backend    | ngl | n_ubatch | fa | mmap | dio |            test |                  t/s |
+| ------------------------------ | ---------: | ---------: | ---------- | --: | -------: | -: | ---: | --: | --------------: | -------------------: |
+| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |          pp2048 |      5948.74 ± 10.61 |
+| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |            tg32 |         81.05 ± 0.20 |
+| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |  pp2048 @ d4096 |      5652.69 ± 34.29 |
+| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |    tg32 @ d4096 |         76.37 ± 0.58 |
+| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |  pp2048 @ d8192 |      5509.57 ± 40.69 |
+| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |    tg32 @ d8192 |         71.61 ± 0.80 |
+| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | CUDA       |  99 |     2048 |  1 |    0 |   1 | pp2048 @ d16384 |      5340.86 ± 36.92 |
+| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |   tg32 @ d16384 |         70.89 ± 0.34 |
+| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | CUDA       |  99 |     2048 |  1 |    0 |   1 | pp2048 @ d32768 |      5023.30 ± 13.52 |
+| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | CUDA       |  99 |     2048 |  1 |    0 |   1 |   tg32 @ d32768 |         62.28 ± 0.30 |
 
-build: eeee367de (6989)
+build: 11fb327bf (7941)
 
+## ggml-org/GLM-4.7-Flash-GGUF
+
+Model: https://huggingface.co/ggml-org/GLM-4.7-Flash-GGUF
+
+- `llama-batched-bench`
+
+
+main: n_kv_max = 270336, n_batch = 2048, n_ubatch = 2048, flash_attn = 1, is_pp_shared = 0, is_tg_separate = 0, n_gpu_layers = -1, n_threads = 20, n_threads_batch = 20
+
+|    PP |     TG |    B |   N_KV |   T_PP s | S_PP t/s |   T_TG s | S_TG t/s |      T s |    S t/s |
+|-------|--------|------|--------|----------|----------|----------|----------|----------|----------|
+|   512 |     32 |    1 |    544 |    0.433 |  1181.83 |    0.693 |    46.16 |    1.126 |   482.94 |
+|   512 |     32 |    2 |   1088 |    0.439 |  2334.46 |    1.034 |    61.89 |    1.473 |   738.75 |
+|   512 |     32 |    4 |   2176 |    0.772 |  2654.46 |    1.459 |    87.76 |    2.230 |   975.77 |
+|   512 |     32 |    8 |   4352 |    1.541 |  2658.78 |    2.043 |   125.31 |    3.583 |  1214.47 |
+|   512 |     32 |   16 |   8704 |    3.083 |  2656.91 |    2.675 |   191.42 |    5.758 |  1511.62 |
+|   512 |     32 |   32 |  17408 |    6.159 |  2660.12 |    3.615 |   283.24 |    9.774 |  1780.98 |
+|  4096 |     32 |    1 |   4128 |    1.915 |  2139.30 |    0.725 |    44.14 |    2.640 |  1563.83 |
+|  4096 |     32 |    2 |   8256 |    3.834 |  2136.40 |    1.119 |    57.21 |    4.953 |  1666.81 |
+|  4096 |     32 |    4 |  16512 |    7.636 |  2145.72 |    1.631 |    78.49 |    9.266 |  1781.93 |
+|  4096 |     32 |    8 |  33024 |   15.295 |  2142.40 |    2.344 |   109.21 |   17.639 |  1872.20 |
+|  4096 |     32 |   16 |  66048 |   30.573 |  2143.62 |    3.773 |   135.70 |   34.346 |  1923.04 |
+|  4096 |     32 |   32 | 132096 |   61.282 |  2138.82 |    5.795 |   176.71 |   67.077 |  1969.31 |
+|  8192 |     32 |    1 |   8224 |    4.510 |  1816.24 |    0.760 |    42.11 |    5.270 |  1560.44 |
+|  8192 |     32 |    2 |  16448 |    9.036 |  1813.19 |    1.206 |    53.06 |   10.242 |  1605.91 |
+|  8192 |     32 |    4 |  32896 |   18.070 |  1813.43 |    1.783 |    71.80 |   19.852 |  1657.03 |
+|  8192 |     32 |    8 |  65792 |   36.125 |  1814.15 |    2.635 |    97.14 |   38.760 |  1697.41 |
+|  8192 |     32 |   16 | 131584 |   72.367 |  1811.20 |    4.954 |   103.34 |   77.322 |  1701.77 |
+|  8192 |     32 |   32 | 263168 |  144.501 |  1814.13 |    8.103 |   126.37 |  152.604 |  1724.51 |
+
+
+- `llama-bench`
+
+| model                          |       size |     params | backend    | ngl | n_ubatch | fa | dio |            test |                  t/s |
+| ------------------------------ | ---------: | ---------: | ---------- | --: | -------: | -: | --: | --------------: | -------------------: |
+| deepseek2 30B.A3B Q8_0         |  29.65 GiB |    29.94 B | CUDA       |  99 |     2048 |  1 |   1 |          pp2048 |      2364.18 ± 11.43 |
+| deepseek2 30B.A3B Q8_0         |  29.65 GiB |    29.94 B | CUDA       |  99 |     2048 |  1 |   1 |            tg32 |         48.68 ± 0.12 |
+| deepseek2 30B.A3B Q8_0         |  29.65 GiB |    29.94 B | CUDA       |  99 |     2048 |  1 |   1 |  pp2048 @ d4096 |       1684.13 ± 1.24 |
+| deepseek2 30B.A3B Q8_0         |  29.65 GiB |    29.94 B | CUDA       |  99 |     2048 |  1 |   1 |    tg32 @ d4096 |         44.62 ± 0.22 |
+| deepseek2 30B.A3B Q8_0         |  29.65 GiB |    29.94 B | CUDA       |  99 |     2048 |  1 |   1 |  pp2048 @ d8192 |       1314.68 ± 1.41 |
+| deepseek2 30B.A3B Q8_0         |  29.65 GiB |    29.94 B | CUDA       |  99 |     2048 |  1 |   1 |    tg32 @ d8192 |         42.59 ± 0.11 |
+| deepseek2 30B.A3B Q8_0         |  29.65 GiB |    29.94 B | CUDA       |  99 |     2048 |  1 |   1 | pp2048 @ d16384 |        914.05 ± 3.32 |
+| deepseek2 30B.A3B Q8_0         |  29.65 GiB |    29.94 B | CUDA       |  99 |     2048 |  1 |   1 |   tg32 @ d16384 |         38.72 ± 0.13 |
+| deepseek2 30B.A3B Q8_0         |  29.65 GiB |    29.94 B | CUDA       |  99 |     2048 |  1 |   1 | pp2048 @ d32768 |        567.20 ± 0.90 |
+| deepseek2 30B.A3B Q8_0         |  29.65 GiB |    29.94 B | CUDA       |  99 |     2048 |  1 |   1 |   tg32 @ d32768 |         32.65 ± 0.09 |
+
+build: 11fb327bf (7941)

benches/mac-m2-ultra/mac-m2-ultra.md

@@ -0,0 +1,298 @@
+## System info
+
+```bash
+uname -a
+Darwin gg-studio 25.2.0 Darwin Kernel Version 25.2.0: Tue Nov 18 21:07:05 PST 2025; root:xnu-12377.61.12~1/RELEASE_ARM64_T6020 arm64
+
+g++ --version
+Apple clang version 17.0.0 (clang-1700.3.19.1)
+Target: arm64-apple-darwin25.2.0
+```
+
+## ggml-org/gpt-oss-20b-GGUF
+
+Model: https://huggingface.co/ggml-org/gpt-oss-20b-GGUF
+
+- `llama-batched-bench`
+
+
+main: n_kv_max = 270336, n_batch = 2048, n_ubatch = 2048, flash_attn = 1, is_pp_shared = 0, is_tg_separate = 0, n_gpu_layers = -1, n_threads = 16, n_threads_batch = 16
+
+|    PP |     TG |    B |   N_KV |   T_PP s | S_PP t/s |   T_TG s | S_TG t/s |      T s |    S t/s |
+|-------|--------|------|--------|----------|----------|----------|----------|----------|----------|
+|   512 |     32 |    1 |    544 |    0.215 |  2381.35 |    0.245 |   130.45 |    0.460 |  1181.81 |
+|   512 |     32 |    2 |   1088 |    0.379 |  2701.43 |    0.382 |   167.56 |    0.761 |  1429.67 |
+|   512 |     32 |    4 |   2176 |    0.721 |  2839.27 |    0.604 |   211.76 |    1.326 |  1641.32 |
+|   512 |     32 |    8 |   4352 |    1.433 |  2858.30 |    1.033 |   247.75 |    2.466 |  1764.57 |
+|   512 |     32 |   16 |   8704 |    2.853 |  2871.12 |    1.570 |   326.11 |    4.423 |  1967.77 |
+|   512 |     32 |   32 |  17408 |    5.699 |  2874.95 |    1.910 |   536.15 |    7.609 |  2287.88 |
+|  4096 |     32 |    1 |   4128 |    1.552 |  2638.56 |    0.334 |    95.72 |    1.887 |  2188.00 |
+|  4096 |     32 |    2 |   8256 |    3.084 |  2655.88 |    0.404 |   158.54 |    3.488 |  2366.86 |
+|  4096 |     32 |    4 |  16512 |    6.151 |  2663.78 |    0.652 |   196.39 |    6.802 |  2427.37 |
+|  4096 |     32 |    8 |  33024 |   12.288 |  2666.77 |    1.135 |   225.47 |   13.423 |  2460.27 |
+|  4096 |     32 |   16 |  66048 |   24.563 |  2668.12 |    1.762 |   290.55 |   26.325 |  2508.97 |
+|  4096 |     32 |   32 | 132096 |   49.114 |  2668.73 |    2.398 |   426.94 |   51.512 |  2564.35 |
+|  8192 |     32 |    1 |   8224 |    3.345 |  2448.78 |    0.275 |   116.46 |    3.620 |  2271.76 |
+|  8192 |     32 |    2 |  16448 |    6.665 |  2458.11 |    0.425 |   150.71 |    7.090 |  2319.91 |
+|  8192 |     32 |    4 |  32896 |   13.315 |  2460.92 |    0.691 |   185.21 |   14.006 |  2348.63 |
+|  8192 |     32 |    8 |  65792 |   26.611 |  2462.73 |    1.212 |   211.16 |   27.823 |  2364.62 |
+|  8192 |     32 |   16 | 131584 |   53.232 |  2462.27 |    1.919 |   266.83 |   55.151 |  2385.88 |
+|  8192 |     32 |   32 | 263168 |  110.455 |  2373.30 |    2.752 |   372.03 |  113.208 |  2324.64 |
+
+
+- `llama-bench`
+
+| model                          |       size |     params | backend    | threads | n_ubatch | fa |            test |                  t/s |
+| ------------------------------ | ---------: | ---------: | ---------- | ------: | -------: | -: | --------------: | -------------------: |
+| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | MTL,BLAS   |      16 |     2048 |  1 |          pp2048 |       2713.40 ± 3.56 |
+| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | MTL,BLAS   |      16 |     2048 |  1 |            tg32 |        129.97 ± 3.90 |
+| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | MTL,BLAS   |      16 |     2048 |  1 |  pp2048 @ d4096 |       2324.59 ± 3.01 |
+| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | MTL,BLAS   |      16 |     2048 |  1 |    tg32 @ d4096 |        123.38 ± 0.17 |
+| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | MTL,BLAS   |      16 |     2048 |  1 |  pp2048 @ d8192 |      1989.82 ± 30.11 |
+| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | MTL,BLAS   |      16 |     2048 |  1 |    tg32 @ d8192 |        117.39 ± 0.33 |
+| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | MTL,BLAS   |      16 |     2048 |  1 | pp2048 @ d16384 |       1556.54 ± 6.22 |
+| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | MTL,BLAS   |      16 |     2048 |  1 |   tg32 @ d16384 |        109.75 ± 0.42 |
+| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | MTL,BLAS   |      16 |     2048 |  1 | pp2048 @ d32768 |       1122.63 ± 1.45 |
+| gpt-oss 20B MXFP4 MoE          |  11.27 GiB |    20.91 B | MTL,BLAS   |      16 |     2048 |  1 |   tg32 @ d32768 |         98.25 ± 0.08 |
+
+build: b828e18c7 (7948)
+
+## ggml-org/gpt-oss-120b-GGUF
+
+Model: https://huggingface.co/ggml-org/gpt-oss-120b-GGUF
+
+- `llama-batched-bench`
+
+
+main: n_kv_max = 270336, n_batch = 2048, n_ubatch = 2048, flash_attn = 1, is_pp_shared = 0, is_tg_separate = 0, n_gpu_layers = -1, n_threads = 16, n_threads_batch = 16
+
+|    PP |     TG |    B |   N_KV |   T_PP s | S_PP t/s |   T_TG s | S_TG t/s |      T s |    S t/s |
+|-------|--------|------|--------|----------|----------|----------|----------|----------|----------|
+|   512 |     32 |    1 |    544 |    0.426 |  1200.92 |    0.361 |    88.56 |    0.788 |   690.64 |
+|   512 |     32 |    2 |   1088 |    0.683 |  1500.14 |    0.545 |   117.35 |    1.228 |   886.02 |
+|   512 |     32 |    4 |   2176 |    1.204 |  1701.56 |    0.847 |   151.19 |    2.050 |  1061.34 |
+|   512 |     32 |    8 |   4352 |    2.402 |  1705.20 |    1.455 |   176.00 |    3.857 |  1128.45 |
+|   512 |     32 |   16 |   8704 |    4.802 |  1705.90 |    2.349 |   217.93 |    7.152 |  1217.08 |
+|   512 |     32 |   32 |  17408 |    9.593 |  1707.85 |    3.665 |   279.42 |   13.258 |  1313.01 |
+|  4096 |     32 |    1 |   4128 |    2.581 |  1587.08 |    0.390 |    82.12 |    2.970 |  1389.67 |
+|  4096 |     32 |    2 |   8256 |    5.124 |  1598.79 |    0.589 |   108.62 |    5.713 |  1445.10 |
+|  4096 |     32 |    4 |  16512 |   10.231 |  1601.47 |    0.928 |   137.98 |   11.158 |  1479.80 |
+|  4096 |     32 |    8 |  33024 |   20.468 |  1600.94 |    1.606 |   159.38 |   22.074 |  1496.04 |
+|  4096 |     32 |   16 |  66048 |   40.924 |  1601.42 |    2.639 |   193.99 |   43.563 |  1516.15 |
+|  4096 |     32 |   32 | 132096 |   81.819 |  1601.98 |    4.466 |   229.29 |   86.284 |  1530.94 |
+|  8192 |     32 |    1 |   8224 |    5.517 |  1484.74 |    0.409 |    78.16 |    5.927 |  1387.58 |
+|  8192 |     32 |    2 |  16448 |   11.008 |  1488.43 |    0.622 |   102.92 |   11.629 |  1414.34 |
+|  8192 |     32 |    4 |  32896 |   22.002 |  1489.29 |    0.987 |   129.66 |   22.990 |  1430.90 |
+|  8192 |     32 |    8 |  65792 |   46.051 |  1423.11 |    1.858 |   137.79 |   47.909 |  1373.27 |
+|  8192 |     32 |   16 | 131584 |   97.680 |  1341.85 |    2.872 |   178.28 |  100.552 |  1308.62 |
+|  8192 |     32 |   32 | 263168 |  176.407 |  1486.02 |    5.048 |   202.85 |  181.455 |  1450.32 |
+
+
+- `llama-bench`
+
+| model                          |       size |     params | backend    | threads | n_ubatch | fa |            test |                  t/s |
+| ------------------------------ | ---------: | ---------: | ---------- | ------: | -------: | -: | --------------: | -------------------: |
+| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | MTL,BLAS   |      16 |     2048 |  1 |          pp2048 |       1648.69 ± 1.80 |
+| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | MTL,BLAS   |      16 |     2048 |  1 |            tg32 |         85.60 ± 0.52 |
+| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | MTL,BLAS   |      16 |     2048 |  1 |  pp2048 @ d4096 |       1429.86 ± 1.01 |
+| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | MTL,BLAS   |      16 |     2048 |  1 |    tg32 @ d4096 |         82.03 ± 0.12 |
+| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | MTL,BLAS   |      16 |     2048 |  1 |  pp2048 @ d8192 |       1257.90 ± 1.81 |
+| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | MTL,BLAS   |      16 |     2048 |  1 |    tg32 @ d8192 |         78.23 ± 0.33 |
+| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | MTL,BLAS   |      16 |     2048 |  1 | pp2048 @ d16384 |       1013.49 ± 0.70 |
+| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | MTL,BLAS   |      16 |     2048 |  1 |   tg32 @ d16384 |         73.20 ± 0.28 |
+| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | MTL,BLAS   |      16 |     2048 |  1 | pp2048 @ d32768 |        721.11 ± 0.58 |
+| gpt-oss 120B MXFP4 MoE         |  59.02 GiB |   116.83 B | MTL,BLAS   |      16 |     2048 |  1 |   tg32 @ d32768 |         65.52 ± 0.10 |
+
+build: b828e18c7 (7948)
+
+## ggml-org/Qwen3-Coder-30B-A3B-Instruct-Q8_0-GGUF
+
+Model: https://huggingface.co/ggml-org/Qwen3-Coder-30B-A3B-Instruct-Q8_0-GGUF
+
+- `llama-batched-bench`
+
+
+main: n_kv_max = 270336, n_batch = 2048, n_ubatch = 2048, flash_attn = 1, is_pp_shared = 0, is_tg_separate = 0, n_gpu_layers = -1, n_threads = 16, n_threads_batch = 16
+
+|    PP |     TG |    B |   N_KV |   T_PP s | S_PP t/s |   T_TG s | S_TG t/s |      T s |    S t/s |
+|-------|--------|------|--------|----------|----------|----------|----------|----------|----------|
+|   512 |     32 |    1 |    544 |    0.243 |  2109.23 |    0.419 |    76.34 |    0.662 |   821.84 |
+|   512 |     32 |    2 |   1088 |    0.406 |  2521.40 |    0.575 |   111.36 |    0.981 |  1109.27 |
+|   512 |     32 |    4 |   2176 |    0.744 |  2751.65 |    0.841 |   152.22 |    1.585 |  1372.71 |
+|   512 |     32 |    8 |   4352 |    1.479 |  2770.20 |    1.330 |   192.48 |    2.809 |  1549.53 |
+|   512 |     32 |   16 |   8704 |    2.951 |  2776.20 |    2.572 |   199.05 |    5.523 |  1575.93 |
+|   512 |     32 |   32 |  17408 |    5.899 |  2777.64 |    2.603 |   393.34 |    8.502 |  2047.54 |
+|  4096 |     32 |    1 |   4128 |    1.901 |  2154.15 |    0.474 |    67.58 |    2.375 |  1738.14 |
+|  4096 |     32 |    2 |   8256 |    3.788 |  2162.89 |    0.652 |    98.17 |    4.439 |  1859.69 |
+|  4096 |     32 |    4 |  16512 |    7.564 |  2166.18 |    0.990 |   129.24 |    8.554 |  1930.34 |
+|  4096 |     32 |    8 |  33024 |   15.121 |  2166.98 |    1.632 |   156.82 |   16.754 |  1971.12 |
+|  4096 |     32 |   16 |  66048 |   30.241 |  2167.09 |    3.166 |   161.72 |   33.407 |  1977.04 |
+|  4096 |     32 |   32 | 132096 |   60.474 |  2167.42 |    3.780 |   270.93 |   64.254 |  2055.86 |
+|  8192 |     32 |    1 |   8224 |    4.733 |  1730.92 |    0.483 |    66.29 |    5.215 |  1576.85 |
+|  8192 |     32 |    2 |  16448 |    9.459 |  1732.09 |    0.722 |    88.58 |   10.182 |  1615.46 |
+|  8192 |     32 |    4 |  32896 |   18.912 |  1732.65 |    1.120 |   114.26 |   20.032 |  1642.14 |
+|  8192 |     32 |    8 |  65792 |   37.797 |  1733.91 |    1.873 |   136.67 |   39.670 |  1658.49 |
+|  8192 |     32 |   16 | 131584 |   84.133 |  1557.92 |    3.718 |   137.72 |   87.850 |  1497.82 |
+|  8192 |     32 |   32 | 263168 |  157.550 |  1663.88 |    4.854 |   210.98 |  162.403 |  1620.46 |
+
+
+- `llama-bench`
+
+| model                          |       size |     params | backend    | threads | n_ubatch | fa |            test |                  t/s |
+| ------------------------------ | ---------: | ---------: | ---------- | ------: | -------: | -: | --------------: | -------------------: |
+| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | MTL,BLAS   |      16 |     2048 |  1 |          pp2048 |       2453.11 ± 1.70 |
+| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | MTL,BLAS   |      16 |     2048 |  1 |            tg32 |         78.97 ± 0.46 |
+| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | MTL,BLAS   |      16 |     2048 |  1 |  pp2048 @ d4096 |       1569.46 ± 1.97 |
+| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | MTL,BLAS   |      16 |     2048 |  1 |    tg32 @ d4096 |         71.18 ± 0.37 |
+| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | MTL,BLAS   |      16 |     2048 |  1 |  pp2048 @ d8192 |       1145.51 ± 1.16 |
+| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | MTL,BLAS   |      16 |     2048 |  1 |    tg32 @ d8192 |         65.11 ± 0.36 |
+| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | MTL,BLAS   |      16 |     2048 |  1 | pp2048 @ d16384 |        741.04 ± 0.74 |
+| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | MTL,BLAS   |      16 |     2048 |  1 |   tg32 @ d16384 |         56.87 ± 0.14 |
+| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | MTL,BLAS   |      16 |     2048 |  1 | pp2048 @ d32768 |        431.31 ± 0.31 |
+| qwen3moe 30B.A3B Q8_0          |  30.25 GiB |    30.53 B | MTL,BLAS   |      16 |     2048 |  1 |   tg32 @ d32768 |         45.26 ± 0.11 |
+
+build: b828e18c7 (7948)
+
+## ggml-org/Qwen2.5-Coder-7B-Q8_0-GGUF
+
+Model: https://huggingface.co/ggml-org/Qwen2.5-Coder-7B-Q8_0-GGUF
+
+- `llama-batched-bench`
+
+
+main: n_kv_max = 270336, n_batch = 2048, n_ubatch = 2048, flash_attn = 1, is_pp_shared = 0, is_tg_separate = 0, n_gpu_layers = -1, n_threads = 16, n_threads_batch = 16
+
+|    PP |     TG |    B |   N_KV |   T_PP s | S_PP t/s |   T_TG s | S_TG t/s |      T s |    S t/s |
+|-------|--------|------|--------|----------|----------|----------|----------|----------|----------|
+|   512 |     32 |    1 |    544 |    0.339 |  1509.22 |    0.409 |    78.17 |    0.749 |   726.67 |
+|   512 |     32 |    2 |   1088 |    0.646 |  1584.93 |    0.483 |   132.45 |    1.129 |   963.45 |
+|   512 |     32 |    4 |   2176 |    1.258 |  1627.50 |    0.585 |   218.67 |    1.844 |  1180.21 |
+|   512 |     32 |    8 |   4352 |    2.506 |  1634.41 |    1.005 |   254.83 |    3.511 |  1239.64 |
+|   512 |     32 |   16 |   8704 |    5.007 |  1635.99 |    1.595 |   321.07 |    6.602 |  1318.38 |
+|   512 |     32 |   32 |  17408 |   10.007 |  1637.19 |    1.676 |   611.12 |   11.683 |  1490.03 |
+|  4096 |     32 |    1 |   4128 |    2.730 |  1500.46 |    0.431 |    74.31 |    3.160 |  1306.12 |
+|  4096 |     32 |    2 |   8256 |    5.446 |  1504.33 |    0.524 |   122.04 |    5.970 |  1382.91 |
+|  4096 |     32 |    4 |  16512 |   10.875 |  1506.59 |    0.662 |   193.45 |   11.537 |  1431.28 |
+|  4096 |     32 |    8 |  33024 |   21.749 |  1506.61 |    1.158 |   221.11 |   22.907 |  1441.64 |
+|  4096 |     32 |   16 |  66048 |   43.477 |  1507.36 |    1.901 |   269.32 |   45.378 |  1455.49 |
+|  4096 |     32 |   32 | 132096 |   86.954 |  1507.37 |    2.325 |   440.42 |   89.279 |  1479.59 |
+|  8192 |     32 |    1 |   8224 |    5.940 |  1379.21 |    0.449 |    71.20 |    6.389 |  1287.20 |
+|  8192 |     32 |    2 |  16448 |   11.865 |  1380.84 |    0.559 |   114.59 |   12.424 |  1323.92 |
+|  8192 |     32 |    4 |  32896 |   23.723 |  1381.25 |    0.728 |   175.80 |   24.452 |  1345.35 |
+|  8192 |     32 |    8 |  65792 |   47.434 |  1381.63 |    1.279 |   200.09 |   48.713 |  1350.60 |
+|  8192 |     32 |   16 | 131584 |   94.864 |  1381.69 |    2.198 |   232.97 |   97.061 |  1355.68 |
+|  8192 |     32 |   32 | 263168 |  189.743 |  1381.57 |    3.052 |   335.50 |  192.795 |  1365.01 |
+
+
+- `llama-bench`
+
+| model                          |       size |     params | backend    | threads | n_ubatch | fa |            test |                  t/s |
+| ------------------------------ | ---------: | ---------: | ---------- | ------: | -------: | -: | --------------: | -------------------: |
+| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | MTL,BLAS   |      16 |     2048 |  1 |          pp2048 |       1565.91 ± 0.86 |
+| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | MTL,BLAS   |      16 |     2048 |  1 |            tg32 |         79.68 ± 0.39 |
+| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | MTL,BLAS   |      16 |     2048 |  1 |  pp2048 @ d4096 |       1317.41 ± 1.02 |
+| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | MTL,BLAS   |      16 |     2048 |  1 |    tg32 @ d4096 |         74.70 ± 0.04 |
+| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | MTL,BLAS   |      16 |     2048 |  1 |  pp2048 @ d8192 |       1134.65 ± 0.76 |
+| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | MTL,BLAS   |      16 |     2048 |  1 |    tg32 @ d8192 |         71.31 ± 0.12 |
+| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | MTL,BLAS   |      16 |     2048 |  1 | pp2048 @ d16384 |        886.46 ± 0.78 |
+| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | MTL,BLAS   |      16 |     2048 |  1 |   tg32 @ d16384 |         65.93 ± 0.06 |
+| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | MTL,BLAS   |      16 |     2048 |  1 | pp2048 @ d32768 |        612.21 ± 0.30 |
+| qwen2 7B Q8_0                  |   7.54 GiB |     7.62 B | MTL,BLAS   |      16 |     2048 |  1 |   tg32 @ d32768 |         56.83 ± 0.02 |
+
+build: b828e18c7 (7948)
+
+## ggml-org/gemma-3-4b-it-qat-GGUF
+
+Model: https://huggingface.co/ggml-org/gemma-3-4b-it-qat-GGUF
+
+- `llama-batched-bench`
+
+
+main: n_kv_max = 270336, n_batch = 2048, n_ubatch = 2048, flash_attn = 1, is_pp_shared = 0, is_tg_separate = 0, n_gpu_layers = -1, n_threads = 16, n_threads_batch = 16
+
+|    PP |     TG |    B |   N_KV |   T_PP s | S_PP t/s |   T_TG s | S_TG t/s |      T s |    S t/s |
+|-------|--------|------|--------|----------|----------|----------|----------|----------|----------|
+|   512 |     32 |    1 |    544 |    0.186 |  2748.06 |    0.235 |   136.28 |    0.421 |  1291.78 |
+|   512 |     32 |    2 |   1088 |    0.342 |  2990.95 |    0.312 |   204.99 |    0.655 |  1662.15 |
+|   512 |     32 |    4 |   2176 |    0.662 |  3092.69 |    0.404 |   316.97 |    1.066 |  2041.21 |
+|   512 |     32 |    8 |   4352 |    1.317 |  3110.41 |    0.579 |   441.80 |    1.896 |  2294.97 |
+|   512 |     32 |   16 |   8704 |    2.625 |  3120.23 |    1.207 |   424.08 |    3.833 |  2270.93 |
+|   512 |     32 |   32 |  17408 |    5.242 |  3125.34 |    1.299 |   788.23 |    6.541 |  2661.19 |
+|  4096 |     32 |    1 |   4128 |    1.408 |  2909.90 |    0.296 |   108.07 |    1.704 |  2422.95 |
+|  4096 |     32 |    2 |   8256 |    2.793 |  2933.40 |    0.325 |   197.00 |    3.118 |  2648.25 |
+|  4096 |     32 |    4 |  16512 |    5.567 |  2943.22 |    0.440 |   291.07 |    6.006 |  2749.05 |
+|  4096 |     32 |    8 |  33024 |   11.114 |  2948.23 |    0.640 |   400.26 |   11.754 |  2809.59 |
+|  4096 |     32 |   16 |  66048 |   22.217 |  2949.76 |    1.327 |   385.83 |   23.544 |  2805.26 |
+|  4096 |     32 |   32 | 132096 |   44.420 |  2950.77 |    1.553 |   659.30 |   45.973 |  2873.36 |
+|  8192 |     32 |    1 |   8224 |    2.860 |  2864.58 |    0.250 |   127.90 |    3.110 |  2644.42 |
+|  8192 |     32 |    2 |  16448 |    5.702 |  2873.63 |    0.335 |   191.07 |    6.036 |  2724.77 |
+|  8192 |     32 |    4 |  32896 |   11.383 |  2878.69 |    0.456 |   280.72 |   11.839 |  2778.63 |
+|  8192 |     32 |    8 |  65792 |   22.750 |  2880.75 |    0.671 |   381.48 |   23.421 |  2809.14 |
+|  8192 |     32 |   16 | 131584 |   45.484 |  2881.74 |    1.406 |   364.04 |   46.890 |  2806.22 |
+|  8192 |     32 |   32 | 263168 |   90.956 |  2882.10 |    1.793 |   570.98 |   92.749 |  2837.41 |
+
+
+- `llama-bench`
+
+| model                          |       size |     params | backend    | threads | n_ubatch | fa |            test |                  t/s |
+| ------------------------------ | ---------: | ---------: | ---------- | ------: | -------: | -: | --------------: | -------------------: |
+| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | MTL,BLAS   |      16 |     2048 |  1 |          pp2048 |       2923.59 ± 3.10 |
+| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | MTL,BLAS   |      16 |     2048 |  1 |            tg32 |        134.28 ± 1.29 |
+| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | MTL,BLAS   |      16 |     2048 |  1 |  pp2048 @ d4096 |       2748.21 ± 3.05 |
+| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | MTL,BLAS   |      16 |     2048 |  1 |    tg32 @ d4096 |        133.11 ± 0.08 |
+| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | MTL,BLAS   |      16 |     2048 |  1 |  pp2048 @ d8192 |       2641.45 ± 2.31 |
+| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | MTL,BLAS   |      16 |     2048 |  1 |    tg32 @ d8192 |        125.85 ± 0.35 |
+| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | MTL,BLAS   |      16 |     2048 |  1 | pp2048 @ d16384 |       2446.20 ± 2.94 |
+| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | MTL,BLAS   |      16 |     2048 |  1 |   tg32 @ d16384 |        125.00 ± 0.12 |
+| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | MTL,BLAS   |      16 |     2048 |  1 | pp2048 @ d32768 |       2129.18 ± 7.43 |
+| gemma3 4B Q4_0                 |   2.35 GiB |     3.88 B | MTL,BLAS   |      16 |     2048 |  1 |   tg32 @ d32768 |        113.14 ± 0.10 |
+
+build: b828e18c7 (7948)
+
+## ggml-org/GLM-4.7-Flash-GGUF
+
+Model: https://huggingface.co/ggml-org/GLM-4.7-Flash-GGUF
+
+- `llama-batched-bench`
+
+
+main: n_kv_max = 270336, n_batch = 2048, n_ubatch = 2048, flash_attn = 1, is_pp_shared = 0, is_tg_separate = 0, n_gpu_layers = -1, n_threads = 16, n_threads_batch = 16
+
+|    PP |     TG |    B |   N_KV |   T_PP s | S_PP t/s |   T_TG s | S_TG t/s |      T s |    S t/s |
+|-------|--------|------|--------|----------|----------|----------|----------|----------|----------|
+|   512 |     32 |    1 |    544 |    0.326 |  1568.69 |    0.522 |    61.28 |    0.849 |   641.09 |
+|   512 |     32 |    2 |   1088 |    0.528 |  1939.42 |    0.744 |    86.07 |    1.272 |   855.63 |
+|   512 |     32 |    4 |   2176 |    0.968 |  2114.85 |    1.105 |   115.85 |    2.073 |  1049.56 |
+|   512 |     32 |    8 |   4352 |    1.928 |  2124.62 |    1.684 |   151.99 |    3.612 |  1204.82 |
+|   512 |     32 |   16 |   8704 |    3.844 |  2131.34 |    3.141 |   162.99 |    6.985 |  1246.11 |
+|   512 |     32 |   32 |  17408 |    7.683 |  2132.38 |    3.924 |   260.95 |   11.608 |  1499.71 |
+|  4096 |     32 |    1 |   4128 |    3.280 |  1248.75 |    0.723 |    44.29 |    4.003 |  1031.33 |
+|  4096 |     32 |    2 |   8256 |    6.545 |  1251.63 |    0.930 |    68.85 |    7.475 |  1104.53 |
+|  4096 |     32 |    4 |  16512 |   13.080 |  1252.64 |    1.454 |    88.03 |   14.534 |  1136.12 |
+|  4096 |     32 |    8 |  33024 |   26.154 |  1252.90 |    2.388 |   107.20 |   28.542 |  1157.04 |
+|  4096 |     32 |   16 |  66048 |   52.297 |  1253.14 |    4.724 |   108.37 |   57.022 |  1158.30 |
+|  4096 |     32 |   32 | 132096 |  104.578 |  1253.34 |    7.266 |   140.93 |  111.844 |  1181.08 |
+|  8192 |     32 |    1 |   8224 |    9.623 |   851.31 |    0.767 |    41.72 |   10.390 |   791.54 |
+|  8192 |     32 |    2 |  16448 |   20.916 |   783.32 |    1.148 |    55.74 |   22.064 |   745.45 |
+|  8192 |     32 |    4 |  32896 |   43.509 |   753.14 |    1.833 |    69.82 |   45.342 |   725.51 |
+|  8192 |     32 |    8 |  65792 |   79.621 |   823.10 |    3.180 |    80.50 |   82.801 |   794.58 |
+|  8192 |     32 |   16 | 131584 |  153.770 |   852.39 |    6.502 |    78.74 |  160.272 |   821.00 |
+|  8192 |     32 |   32 | 263168 |  307.539 |   852.39 |   10.839 |    94.48 |  318.378 |   826.59 |
+
+
+- `llama-bench`
+
+| model                          |       size |     params | backend    | threads | n_ubatch | fa |            test |                  t/s |
+| ------------------------------ | ---------: | ---------: | ---------- | ------: | -------: | -: | --------------: | -------------------: |
+| deepseek2 30B.A3B Q8_0         |  29.65 GiB |    29.94 B | MTL,BLAS   |      16 |     2048 |  1 |          pp2048 |       1629.33 ± 0.27 |
+| deepseek2 30B.A3B Q8_0         |  29.65 GiB |    29.94 B | MTL,BLAS   |      16 |     2048 |  1 |            tg32 |         59.58 ± 0.13 |
+| deepseek2 30B.A3B Q8_0         |  29.65 GiB |    29.94 B | MTL,BLAS   |      16 |     2048 |  1 |  pp2048 @ d4096 |        732.67 ± 0.42 |
+| deepseek2 30B.A3B Q8_0         |  29.65 GiB |    29.94 B | MTL,BLAS   |      16 |     2048 |  1 |    tg32 @ d4096 |         47.44 ± 0.15 |
+| deepseek2 30B.A3B Q8_0         |  29.65 GiB |    29.94 B | MTL,BLAS   |      16 |     2048 |  1 |  pp2048 @ d8192 |        474.33 ± 0.33 |
+| deepseek2 30B.A3B Q8_0         |  29.65 GiB |    29.94 B | MTL,BLAS   |      16 |     2048 |  1 |    tg32 @ d8192 |         40.20 ± 0.20 |
+| deepseek2 30B.A3B Q8_0         |  29.65 GiB |    29.94 B | MTL,BLAS   |      16 |     2048 |  1 | pp2048 @ d16384 |        277.46 ± 0.09 |
+| deepseek2 30B.A3B Q8_0         |  29.65 GiB |    29.94 B | MTL,BLAS   |      16 |     2048 |  1 |   tg32 @ d16384 |         31.50 ± 0.93 |
+| deepseek2 30B.A3B Q8_0         |  29.65 GiB |    29.94 B | MTL,BLAS   |      16 |     2048 |  1 | pp2048 @ d32768 |        151.44 ± 0.05 |
+| deepseek2 30B.A3B Q8_0         |  29.65 GiB |    29.94 B | MTL,BLAS   |      16 |     2048 |  1 |   tg32 @ d32768 |         21.81 ± 0.01 |
+
+build: b828e18c7 (7948)

ci/run.sh

@@ -635,6 +635,29 @@ function gg_check_build_requirements {
     fi
 }
 
+function gg_run_test_backend_ops_cpu {
+    cd ${SRC}
+
+    cd build-ci-release
+
+    set -e
+
+    (time ./bin/test-backend-ops -b CPU ) 2>&1 | tee -a $OUT/${ci}-test-backend-ops-cpu.log
+
+    set +e
+}
+
+function gg_sum_test_backend_ops_cpu {
+    gg_printf '### %s\n\n' "${ci}"
+
+    gg_printf 'Runs test-backend-ops for CPU backend\n'
+    gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
+    gg_printf '```\n'
+    gg_printf '%s\n' "$(cat $OUT/${ci}-test-backend-ops-cpu.log)"
+    gg_printf '```\n'
+    gg_printf '\n'
+}
+
 ## main
 
 export LLAMA_LOG_PREFIX=1
@@ -663,6 +686,10 @@ ret=0
 test $ret -eq 0 && gg_run ctest_debug
 test $ret -eq 0 && gg_run ctest_release
 
+if [ ! -z ${GG_BUILD_HIGH_PERF} ]; then
+    test $ret -eq 0 && gg_run test_backend_ops_cpu
+fi
+
 if [ -z ${GG_BUILD_LOW_PERF} ]; then
     test $ret -eq 0 && gg_run embd_bge_small
     test $ret -eq 0 && gg_run rerank_tiny

cmake/common.cmake

@@ -32,4 +32,27 @@ function(llama_add_compile_flags)
             set(CXX_FLAGS "" PARENT_SCOPE)
         endif()
     endif()
+
+    if (NOT MSVC)
+        if (LLAMA_SANITIZE_THREAD)
+            message(STATUS "Using -fsanitize=thread")
+
+            add_compile_options(-fsanitize=thread)
+            link_libraries     (-fsanitize=thread)
+        endif()
+
+        if (LLAMA_SANITIZE_ADDRESS)
+            message(STATUS "Using -fsanitize=address")
+
+            add_compile_options(-fsanitize=address -fno-omit-frame-pointer)
+            link_libraries     (-fsanitize=address)
+        endif()
+
+        if (LLAMA_SANITIZE_UNDEFINED)
+            message(STATUS "Using -fsanitize=undefined")
+
+            add_compile_options(-fsanitize=undefined)
+            link_libraries     (-fsanitize=undefined)
+        endif()
+    endif()
 endfunction()

common/CMakeLists.txt

@@ -73,6 +73,10 @@ add_library(${TARGET} STATIC
     log.h
     ngram-cache.cpp
     ngram-cache.h
+    ngram-map.cpp
+    ngram-map.h
+    ngram-mod.cpp
+    ngram-mod.h
     peg-parser.cpp
     peg-parser.h
     preset.cpp

common/arg.cpp

@@ -6,6 +6,7 @@
 #include "json-schema-to-grammar.h"
 #include "log.h"
 #include "sampling.h"
+#include "speculative.h"
 #include "preset.h"
 
 // fix problem with std::min and std::max
@@ -579,14 +580,14 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
             params.mmproj = res.mmproj;
         }
         // only download mmproj if the current example is using it
-        for (auto & ex : mmproj_examples) {
+        for (const auto & ex : mmproj_examples) {
             if (ctx_arg.ex == ex) {
                 common_params_handle_model(params.mmproj,    params.hf_token, params.offline);
                 break;
             }
         }
-        common_params_handle_model(params.speculative.model, params.hf_token, params.offline);
-        common_params_handle_model(params.vocoder.model,     params.hf_token, params.offline);
+        common_params_handle_model(params.speculative.mparams_dft, params.hf_token, params.offline);
+        common_params_handle_model(params.vocoder.model,           params.hf_token, params.offline);
     }
 
     // model is required (except for server)
@@ -1216,16 +1217,16 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         {"-lcs", "--lookup-cache-static"}, "FNAME",
         "path to static lookup cache to use for lookup decoding (not updated by generation)",
         [](common_params & params, const std::string & value) {
-            params.lookup_cache_static = value;
+            params.speculative.lookup_cache_static = value;
         }
-    ).set_examples({LLAMA_EXAMPLE_LOOKUP}));
+    ).set_examples({LLAMA_EXAMPLE_LOOKUP, LLAMA_EXAMPLE_SERVER}));
     add_opt(common_arg(
         {"-lcd", "--lookup-cache-dynamic"}, "FNAME",
         "path to dynamic lookup cache to use for lookup decoding (updated by generation)",
         [](common_params & params, const std::string & value) {
-            params.lookup_cache_dynamic = value;
+            params.speculative.lookup_cache_dynamic = value;
         }
-    ).set_examples({LLAMA_EXAMPLE_LOOKUP}));
+    ).set_examples({LLAMA_EXAMPLE_LOOKUP, LLAMA_EXAMPLE_SERVER}));
     add_opt(common_arg(
         {"-c", "--ctx-size"}, "N",
         string_format("size of the prompt context (default: %d, 0 = loaded from model)", params.n_ctx),
@@ -1300,7 +1301,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params, bool value) {
             params.kv_unified = value;
         }
-    ).set_env("LLAMA_ARG_KV_UNIFIED").set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_PERPLEXITY, LLAMA_EXAMPLE_BATCHED}));
+    ).set_env("LLAMA_ARG_KV_UNIFIED").set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_PERPLEXITY, LLAMA_EXAMPLE_BATCHED, LLAMA_EXAMPLE_BENCH, LLAMA_EXAMPLE_PARALLEL}));
     add_opt(common_arg(
         {"--context-shift"},
         {"--no-context-shift"},
@@ -2330,19 +2331,21 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         }
     ).set_env("LLAMA_ARG_N_GPU_LAYERS"));
     add_opt(common_arg(
-        {"-sm", "--split-mode"}, "{none,layer,row}",
+        {"-sm", "--split-mode"}, "{none,layer,row,tensor}",
         "how to split the model across multiple GPUs, one of:\n"
         "- none: use one GPU only\n"
-        "- layer (default): split layers and KV across GPUs\n"
-        "- row: split rows across GPUs",
+        "- layer (default): split layers and KV across GPUs (pipelined)\n"
+        "- row: split weight across GPUs by rows (parallelized)\n"
+        "- tensor: split weights and KV across GPUs (parallelized)",
         [](common_params & params, const std::string & value) {
-            std::string arg_next = value;
-            if (arg_next == "none") {
+            if (value == "none") {
                 params.split_mode = LLAMA_SPLIT_MODE_NONE;
-            } else if (arg_next == "layer") {
+            } else if (value == "layer") {
                 params.split_mode = LLAMA_SPLIT_MODE_LAYER;
-            } else if (arg_next == "row") {
+            } else if (value == "row") {
                 params.split_mode = LLAMA_SPLIT_MODE_ROW;
+            } else if (value == "tensor") {
+                params.split_mode = LLAMA_SPLIT_MODE_TENSOR;
             } else {
                 throw std::invalid_argument("invalid value");
             }
@@ -2563,7 +2566,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         {"-hfd", "-hfrd", "--hf-repo-draft"}, "<user>/<model>[:quant]",
         "Same as --hf-repo, but for the draft model (default: unused)",
         [](common_params & params, const std::string & value) {
-            params.speculative.model.hf_repo = value;
+            params.speculative.mparams_dft.hf_repo = value;
         }
     ).set_env("LLAMA_ARG_HFD_REPO"));
     add_opt(common_arg(
@@ -3384,7 +3387,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         {"-md", "--model-draft"}, "FNAME",
         "draft model for speculative decoding (default: unused)",
         [](common_params & params, const std::string & value) {
-            params.speculative.model.path = value;
+            params.speculative.mparams_dft.path = value;
         }
     ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_MODEL_DRAFT"));
     add_opt(common_arg(
@@ -3394,6 +3397,58 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.speculative.replacements.push_back({ tgt, dft });
         }
     ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}));
+    add_opt(common_arg(
+        {"--spec-type"}, "[none|ngram-cache|ngram-simple|ngram-map-k|ngram-map-k4v|ngram-mod]",
+        string_format("type of speculative decoding to use when no draft model is provided (default: %s)\n",
+            common_speculative_type_to_str(params.speculative.type).c_str()),
+        [](common_params & params, const std::string & value) {
+            if (value == "none") {
+                params.speculative.type = COMMON_SPECULATIVE_TYPE_NONE;
+            } else if (value == "ngram-cache") {
+                params.speculative.type = COMMON_SPECULATIVE_TYPE_NGRAM_CACHE;
+            } else if (value == "ngram-simple") {
+                params.speculative.type = COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE;
+            } else if (value == "ngram-map-k") {
+                params.speculative.type = COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K;
+            } else if (value == "ngram-map-k4v") {
+                params.speculative.type = COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V;
+            } else if (value == "ngram-mod") {
+                params.speculative.type = COMMON_SPECULATIVE_TYPE_NGRAM_MOD;
+            } else {
+                throw std::invalid_argument("unknown speculative decoding type without draft model");
+            }
+        }
+    ).set_examples({LLAMA_EXAMPLE_SERVER}));
+    add_opt(common_arg(
+        {"--spec-ngram-size-n"}, "N",
+        string_format("ngram size N for ngram-simple/ngram-map speculative decoding, length of lookup n-gram (default: %d)", params.speculative.ngram_size_n),
+        [](common_params & params, int value) {
+            if (value < 1 || value > 1024) {
+                throw std::invalid_argument("ngram size N must be between 1 and 1024 inclusive");
+            }
+            params.speculative.ngram_size_n = value;
+        }
+    ).set_examples({LLAMA_EXAMPLE_SERVER}));
+    add_opt(common_arg(
+        {"--spec-ngram-size-m"}, "N",
+        string_format("ngram size M for ngram-simple/ngram-map speculative decoding, length of draft m-gram (default: %d)", params.speculative.ngram_size_m),
+        [](common_params & params, int value) {
+            if (value < 1 || value > 1024) {
+                throw std::invalid_argument("ngram size M must be between 1 and 1024 inclusive");
+            }
+            params.speculative.ngram_size_m = value;
+        }
+    ).set_examples({LLAMA_EXAMPLE_SERVER}));
+    add_opt(common_arg(
+        {"--spec-ngram-min-hits"}, "N",
+        string_format("minimum hits for ngram-map speculative decoding (default: %d)", params.speculative.ngram_min_hits),
+        [](common_params & params, int value) {
+            if (value < 1) {
+                throw std::invalid_argument("ngram min hits must be at least 1");
+            }
+            params.speculative.ngram_min_hits = value;
+        }
+    ).set_examples({LLAMA_EXAMPLE_SERVER}));
     add_opt(common_arg(
         {"-ctkd", "--cache-type-k-draft"}, "TYPE",
         string_format(
@@ -3620,8 +3675,8 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params) {
             params.model.hf_repo = "ggml-org/Qwen2.5-Coder-7B-Q8_0-GGUF";
             params.model.hf_file = "qwen2.5-coder-7b-q8_0.gguf";
-            params.speculative.model.hf_repo = "ggml-org/Qwen2.5-Coder-0.5B-Q8_0-GGUF";
-            params.speculative.model.hf_file = "qwen2.5-coder-0.5b-q8_0.gguf";
+            params.speculative.mparams_dft.hf_repo = "ggml-org/Qwen2.5-Coder-0.5B-Q8_0-GGUF";
+            params.speculative.mparams_dft.hf_file = "qwen2.5-coder-0.5b-q8_0.gguf";
             params.port = 8012;
             params.n_ubatch = 1024;
             params.n_batch = 1024;
@@ -3636,8 +3691,8 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params) {
             params.model.hf_repo = "ggml-org/Qwen2.5-Coder-14B-Q8_0-GGUF";
             params.model.hf_file = "qwen2.5-coder-14b-q8_0.gguf";
-            params.speculative.model.hf_repo = "ggml-org/Qwen2.5-Coder-0.5B-Q8_0-GGUF";
-            params.speculative.model.hf_file = "qwen2.5-coder-0.5b-q8_0.gguf";
+            params.speculative.mparams_dft.hf_repo = "ggml-org/Qwen2.5-Coder-0.5B-Q8_0-GGUF";
+            params.speculative.mparams_dft.hf_file = "qwen2.5-coder-0.5b-q8_0.gguf";
             params.port = 8012;
             params.n_ubatch = 1024;
             params.n_batch = 1024;

common/chat.cpp

@@ -380,15 +380,46 @@ std::vector<common_chat_msg> common_chat_msgs_parse_oaicompat(const json & messa
     return msgs;
 }
 
-json common_chat_msgs_to_json_oaicompat(const std::vector<common_chat_msg> & msgs, bool concat_typed_text) {
+static json render_message_to_json(const std::vector<common_chat_msg> & msgs, const jinja::caps & c) {
+    if (!c.supports_string_content && !c.supports_typed_content) {
+        LOG_WRN("%s: Neither string content nor typed content is supported by the template. This is unexpected and may lead to issues.\n", __func__);
+    }
+
+    bool only_string_accepted =  c.supports_string_content && !c.supports_typed_content;
+    bool only_typed_accepted  = !c.supports_string_content &&  c.supports_typed_content;
+
     json messages = json::array();
     for (const auto & msg : msgs) {
-        json jmsg = msg.to_json_oaicompat(concat_typed_text);
-        messages.push_back(jmsg);
+        if (only_string_accepted) {
+            json jmsg = msg.to_json_oaicompat(/* concat_typed_text= */ true);
+            messages.push_back(jmsg);
+        } else if (only_typed_accepted) {
+            json jmsg = msg.to_json_oaicompat(/* concat_typed_text= */ false);
+            if (jmsg.at("content").is_string()) {
+                jmsg["content"] = json::array({
+                    json{
+                        {"type", "text"},
+                        {"text", jmsg.at("content").get<std::string>()},
+                    }
+                });
+            }
+            messages.push_back(jmsg);
+        } else {
+            json jmsg = msg.to_json_oaicompat(/* concat_typed_text= */ false);
+            messages.push_back(jmsg);
+        }
     }
     return messages;
 }
 
+// DEPRECATED: only used in tests
+json common_chat_msgs_to_json_oaicompat(const std::vector<common_chat_msg> & msgs, bool concat_typed_text) {
+    jinja::caps c;
+    c.supports_string_content = true;
+    c.supports_typed_content = !concat_typed_text;
+    return render_message_to_json(msgs, c);
+}
+
 std::vector<common_chat_tool> common_chat_tools_parse_oaicompat(const json & tools) {
     std::vector<common_chat_tool> result;
 
@@ -771,10 +802,12 @@ static std::string apply(
 
     nlohmann::ordered_json inp = nlohmann::ordered_json{
         {"messages", messages_override.has_value() ? *messages_override : inputs.messages},
-        {"tools", tools_override.has_value() ? *tools_override : inputs.tools},
         {"bos_token", tmpl.bos_token()},
         {"eos_token", tmpl.eos_token()},
     };
+    if (tools_override.has_value() || !inputs.tools.empty()) {
+        inp["tools"] = tools_override.has_value() ? *tools_override : inputs.tools;
+    }
     if (inputs.extra_context.is_object()) {
         // TODO: do we need to merge, or replacing is fine?
         for (const auto & [k, v] : inputs.extra_context.items()) {
@@ -790,9 +823,6 @@ static std::string apply(
     if (inputs.add_generation_prompt) {
         inp["add_generation_prompt"] = true;
     }
-    if (inp["tools"].is_null()) {
-        inp["tools"] = json::array();
-    }
 
     jinja::global_from_json(ctx, inp, inputs.mark_input);
 
@@ -2219,12 +2249,11 @@ static common_chat_params common_chat_params_init_glm_4_5(const common_chat_temp
 static common_chat_params common_chat_params_init_firefunction_v2(const common_chat_template & tmpl, const struct templates_params & inputs) {
     LOG_DBG("%s\n", __func__);
     common_chat_params data;
-    const std::optional<json> tools_override = json();
     const std::optional<json> additional_context = json {
         {"datetime", format_time(inputs.now, "%b %d %Y %H:%M:%S GMT")},
         {"functions", json(inputs.tools.empty() ? "" : inputs.tools.dump(2))},
     };
-    data.prompt = apply(tmpl, inputs, /* messages_override =*/ std::nullopt, tools_override, additional_context);
+    data.prompt = apply(tmpl, inputs, /* messages_override =*/ std::nullopt, /* tools_override =*/ std::nullopt, additional_context);
     if (inputs.tools.is_array() && !inputs.tools.empty()) {
         data.grammar_lazy = inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED;
         data.grammar = build_grammar([&](const common_grammar_builder & builder) {
@@ -2573,20 +2602,165 @@ static common_chat_params common_chat_params_init_granite(const common_chat_temp
 static common_chat_params common_chat_params_init_solar_open(const common_chat_template & tmpl, const struct templates_params & inputs) {
     common_chat_params data;
 
-    // TODO: Reasoning effort
-    json additional_context = {};
+    // Copy `reasoning_content` to `reasoning`
+    auto adjusted_messages = json::array();
+    for (const auto & msg : inputs.messages) {
+        if (msg.contains("reasoning_content") && msg.at("reasoning_content").is_string()) {
+            auto adjusted_message = msg;
+            adjusted_message["reasoning"] = msg.at("reasoning_content");
+            adjusted_message.erase("reasoning_content");
+            adjusted_messages.push_back(adjusted_message);
+        } else {
+            adjusted_messages.push_back(msg);
+        }
+    }
 
-    data.prompt = apply(tmpl, inputs, std::nullopt, std::nullopt, additional_context);
-    data.format = COMMON_CHAT_FORMAT_SOLAR_OPEN;
+    auto has_tools = inputs.tools.is_array() && !inputs.tools.empty();
+    auto include_grammar = true;
 
+    auto prompt = apply(tmpl, inputs, /* messages_override= */ adjusted_messages);
+
+    // Check if we need to replace the flush token with end token during inference and without generation prompt.
+    if (inputs.is_inference && !inputs.add_generation_prompt) {
+        static constexpr std::string_view return_token = "<|flush|>";
+        static constexpr std::string_view end_token    = "<|end|>";
+        if (size_t pos = prompt.rfind(return_token); pos != std::string::npos) {
+            prompt.replace(pos, return_token.length(), end_token);
+        }
+    }
+
+    data.prompt = prompt;
+    data.format = COMMON_CHAT_FORMAT_PEG_NATIVE;
     data.preserved_tokens = {
         "<|think|>",
         "<|content|>",
         "<|begin|>",
         "<|end|>",
+        "<|tool_calls|>",
+        "<|tool_call:begin|>",
+        "<|tool_call:end|>",
+        "<|tool_call:name|>",
+        "<|tool_call:args|>",
     };
 
-    // TODO: Tool calling
+    auto parser = build_chat_peg_native_parser([&](common_chat_peg_native_builder & p) {
+        auto lit_think = p.atomic(p.literal("<|think|>"));
+        auto lit_assistant_begin = p.atomic(p.literal("<|begin|>assistant"));
+        auto lit_content = p.atomic(p.literal("<|content|>"));
+        auto lit_end = p.atomic(p.literal("<|end|>"));
+        auto parser_until_end = p.until("<|end|>");
+
+        // reasoning <- "<|think|>" (!"<|end|>" .)*
+        auto parser_reasoning = p.rule("reasoning", lit_think + p.reasoning(parser_until_end));
+
+        // content <- "<|content|>" (!"<|end|>" .)*
+        auto parser_content = p.rule("content", lit_content + p.content(parser_until_end));
+
+        // wrap_choice(items) <- item-choice wrapped*
+        // item-choice        <- items[0] / ... / items[n]
+        // wrapped            <- "<|end|><|begin|>assistant" item-choice
+        auto wrap_choice = [&](const std::vector<common_peg_parser> & items) {
+            auto choice = p.choice(items);
+            return choice + p.zero_or_more(lit_end + lit_assistant_begin + choice);
+        };
+
+        // wrap_seq(items) <- item[0] "<|end|><|begin|>assistant" item[1] ...
+        auto wrap_seq = [&](const std::vector<common_peg_parser> & items) {
+            auto seq = p.sequence();
+            for (auto i = 0u; i < items.size(); i++) {
+                if (i == 0) {
+                    seq += items[i];
+                    continue;
+                }
+                seq += lit_end + lit_assistant_begin + items[i];
+            }
+            return seq;
+        };
+
+        // Response format parser
+        if (inputs.json_schema.is_object() && !inputs.json_schema.empty()) {
+            auto parser_response_format = lit_content + p.content(p.schema(p.json(), "response-format", inputs.json_schema));
+            return p.choice({
+                wrap_seq({parser_reasoning, parser_response_format}),
+                wrap_seq({parser_response_format})
+            });
+        }
+
+        auto lit_tool_call_begin = p.literal("<|tool_call:begin|>");
+        auto lit_tool_call_name = p.literal("<|tool_call:name|>");
+        auto lit_tool_call_args = p.literal("<|tool_call:args|>");
+        auto lit_tool_call_end = p.literal("<|tool_call:end|>");
+
+        // Tool call parser
+        if (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE) {
+            auto parser_tool_call = p.choice();
+            foreach_function(inputs.tools, [&](const json & tool) {
+                const auto & function = tool.at("function");
+                std::string name = function.at("name");
+                const auto & schema = function.at("parameters");
+
+                // tool(name, schema) <- name "<|tool_call:args|>" schema
+                parser_tool_call |= p.rule("tool-" + name,
+                    p.atomic(p.tool_name(p.literal(name)) + lit_tool_call_args)
+                    + p.tool_args(p.schema(p.json(), "tool-" + name + "-schema", schema)));
+            });
+
+            auto min_calls = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED ? 1 : 0;
+            auto max_calls = inputs.parallel_tool_calls ? -1 : 1;
+
+            // tool-calls  <- "<|tool_calls|>" tool-call+
+            // tool-call   <- "<|tool_call:begin|> call-id "<|tool_call:name|>" &([^<]+ "<|tool_call:args|>") tool-choice "<|tool_call:end|>"
+            // call-id     <- [a-zA-Z0-9_-]+
+            // tool-choice <- tool(t[0].name, t[0].schema) / ... / tool(t[n].name, t[n].schema)
+            auto parser_tool_calls = p.trigger_rule("tool-calls",
+                p.atomic(p.literal("<|tool_calls|>"))
+                + p.repeat(
+                    p.tool_open(
+                        lit_tool_call_begin
+                        + p.tool_id(p.chars("[a-zA-Z0-9_-]", 1, -1))
+                        + lit_tool_call_name
+                        + p.peek(p.chars("[^<]", 1, -1) + lit_tool_call_args))
+                    + parser_tool_call
+                    + p.tool_close(lit_tool_call_end),
+                /* min = */ 1,
+                /* max = */ max_calls));
+
+            if (min_calls == 1) {
+                // If required, then try any combination of the reasoning, content, and tool call
+                return p.choice({
+                    wrap_seq({parser_reasoning, parser_content, parser_tool_calls}),
+                    wrap_seq({parser_reasoning, parser_tool_calls}),
+                    wrap_seq({parser_content, parser_tool_calls}),
+                    wrap_seq({parser_tool_calls})
+                });
+            }
+
+            return wrap_choice({parser_reasoning, parser_content, parser_tool_calls});
+        }
+
+        // Content only parser
+        include_grammar = false;
+        return wrap_choice({parser_reasoning, parser_content});
+    });
+
+    data.parser = parser.save();
+
+    if (include_grammar) {
+        data.grammar_lazy = has_tools && inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_AUTO;
+
+        data.grammar = build_grammar([&](const common_grammar_builder & builder) {
+            foreach_function(inputs.tools, [&](const json & tool) {
+                const auto & function = tool.at("function");
+                auto schema = function.at("parameters");
+                builder.resolve_refs(schema);
+            });
+            parser.build_grammar(builder, data.grammar_lazy);
+        });
+
+        data.grammar_triggers = {
+            {COMMON_GRAMMAR_TRIGGER_TYPE_WORD, "<|tool_calls|>"}
+        };
+    }
 
     return data;
 }
@@ -2877,7 +3051,7 @@ static common_chat_params common_chat_templates_apply_jinja(
         : *tmpls->template_default;
     const auto & src = tmpl.source();
     const auto & caps = tmpl.original_caps();
-    params.messages = common_chat_msgs_to_json_oaicompat(inputs.messages, /* concat_text= */ !tmpl.original_caps().requires_typed_content);
+    params.messages = render_message_to_json(inputs.messages, tmpl.original_caps());
     params.add_generation_prompt = inputs.add_generation_prompt;
     params.tool_choice = inputs.tool_choice;
     params.reasoning_format = inputs.reasoning_format;
@@ -3043,6 +3217,13 @@ static common_chat_params common_chat_templates_apply_jinja(
         return common_chat_params_init_apriel_1_5(tmpl, params);
     }
 
+    // Solar Open
+    if (src.find("<|tool_response:begin|>") != std::string::npos &&
+        src.find("<|tool_response:name|>") != std::string::npos &&
+        src.find("<|tool_response:result|>") != std::string::npos) {
+        return common_chat_params_init_solar_open(tmpl, params);
+    }
+
     // Use generic handler when mixing tools + JSON schema.
     // TODO: support that mix in handlers below.
     if ((params.tools.is_array() && params.json_schema.is_object())) {

common/chat.h

@@ -240,6 +240,8 @@ bool common_chat_templates_support_enable_thinking(const common_chat_templates *
 
 // Parses a JSON array of messages in OpenAI's chat completion API format.
 std::vector<common_chat_msg> common_chat_msgs_parse_oaicompat(const nlohmann::ordered_json & messages);
+
+// DEPRECATED: only used in tests
 nlohmann::ordered_json common_chat_msgs_to_json_oaicompat(const std::vector<common_chat_msg> & msgs, bool concat_typed_text = false);
 
 std::vector<common_chat_tool> common_chat_tools_parse_oaicompat(const nlohmann::ordered_json & tools);

common/common.cpp

@@ -1097,7 +1097,10 @@ common_init_result::common_init_result(common_params & params) :
     if (params.fit_params) {
         LOG_INF("%s: fitting params to device memory, for bugs during this step try to reproduce them with -fit off, or provide --verbose logs if the bug only occurs with -fit on\n", __func__);
         llama_params_fit(params.model.path.c_str(), &mparams, &cparams,
-            params.tensor_split, params.tensor_buft_overrides.data(), params.fit_params_target.data(), params.fit_params_min_ctx,
+            params.tensor_split,
+            params.tensor_buft_overrides.data(),
+            params.fit_params_target.data(),
+            params.fit_params_min_ctx,
             params.verbosity >= 4 ? GGML_LOG_LEVEL_DEBUG : GGML_LOG_LEVEL_ERROR);
     }
 
@@ -1208,10 +1211,6 @@ std::vector<llama_adapter_lora_ptr> & common_init_result::lora() {
     return pimpl->lora;
 }
 
-void common_init_result::free_context() {
-    pimpl->context.reset();
-}
-
 common_init_result_ptr common_init_from_params(common_params & params) {
     common_init_result_ptr res(new common_init_result(params));
 

common/common.h

@@ -164,6 +164,17 @@ enum common_params_sampling_config : uint64_t {
     COMMON_PARAMS_SAMPLING_CONFIG_MIROSTAT_ETA    = 1 << 11,
 };
 
+enum common_speculative_type {
+    COMMON_SPECULATIVE_TYPE_NONE,          // no speculative decoding
+    COMMON_SPECULATIVE_TYPE_DRAFT,         // draft model
+    COMMON_SPECULATIVE_TYPE_EAGLE3,        // eagle draft model
+    COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE,  // simple self-speculative decoding
+    COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K,   // self-speculative decoding with n-gram keys only
+    COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V, // self-speculative decoding with n-gram keys and 4 m-gram values
+    COMMON_SPECULATIVE_TYPE_NGRAM_MOD,
+    COMMON_SPECULATIVE_TYPE_NGRAM_CACHE,   // self-speculative decoding with 3-level n-gram cache
+    COMMON_SPECULATIVE_TYPE_COUNT          // number of types, unknown type
+};
 
 // sampling parameters
 struct common_params_sampling {
@@ -242,17 +253,39 @@ struct common_params_model {
     std::string name        = ""; // in format <user>/<model>[:<tag>] (tag is optional)     // NOLINT
 };
 
-struct common_params_speculative {
-    std::vector<ggml_backend_dev_t> devices; // devices to use for offloading
+struct common_ngram_mod;
 
-    int32_t n_ctx        =     0; // draft context size
-    int32_t n_max        =    16; // maximum number of tokens to draft during speculative decoding
-    int32_t n_min        =     0; // minimum number of draft tokens to use for speculative decoding
-    int32_t n_gpu_layers =    -1; // number of layers to store in VRAM for the draft model (-1 - use default)
-    float   p_split      =  0.1f; // speculative decoding split probability
-    float   p_min        = 0.75f; // minimum speculative decoding probability (greedy)
-    std::vector<std::pair<std::string, std::string>> replacements; // main to speculative model replacements
-    std::vector<llama_model_tensor_buft_override> tensor_buft_overrides;
+struct common_params_speculative {
+    common_speculative_type type = COMMON_SPECULATIVE_TYPE_NONE; // type of speculative decoding
+
+    // general-purpose speculative decoding parameters
+
+    int32_t n_max   = 16; // maximum number of tokens to draft during speculative decoding
+    int32_t n_min   = 0; // minimum number of draft tokens to use for speculative decoding
+    float   p_split = 0.1f; // speculative decoding split probability
+    float   p_min   = 0.75f; // minimum speculative decoding probability (greedy)
+
+    // ngram-based speculative decoding
+
+    uint16_t ngram_size_n     = 12; // ngram size for lookup
+    uint16_t ngram_size_m     = 48; // mgram size for speculative tokens
+    uint16_t ngram_min_hits   =  1; // minimum hits at ngram/mgram lookup for mgram to be proposed
+
+    std::shared_ptr<common_ngram_mod> ngram_mod;
+
+    std::string lookup_cache_static;  // path of static ngram cache file for lookup decoding           // NOLINT
+    std::string lookup_cache_dynamic; // path of dynamic ngram cache file for lookup decoding          // NOLINT
+
+    // draft-model speculative decoding
+
+    struct common_params_model mparams_dft;
+
+    llama_model * model_dft = nullptr; // a llama_model that can be shared by multiple speculative contexts
+
+    llama_context_params cparams_dft; // these are the parameters for the draft llama_context
+
+    int32_t n_ctx        = 0;  // draft context size
+    int32_t n_gpu_layers = -1; // number of layers to store in VRAM for the draft model (-1 - use default)
 
     ggml_type cache_type_k = GGML_TYPE_F16; // KV cache data type for the K
     ggml_type cache_type_v = GGML_TYPE_F16; // KV cache data type for the V
@@ -260,7 +293,14 @@ struct common_params_speculative {
     struct cpu_params cpuparams;
     struct cpu_params cpuparams_batch;
 
-    struct common_params_model model;
+    std::vector<ggml_backend_dev_t> devices; // devices to use for offloading
+
+    std::vector<std::pair<std::string, std::string>> replacements; // main to speculative model replacements
+    std::vector<llama_model_tensor_buft_override> tensor_buft_overrides;
+
+    bool has_dft() const {
+        return !mparams_dft.path.empty() || !mparams_dft.hf_repo.empty();
+    }
 };
 
 struct common_params_vocoder {
@@ -378,8 +418,6 @@ struct common_params {
     std::string path_prompt_cache    = ""; // path to file for saving/loading prompt eval state             // NOLINT
     std::string input_prefix         = ""; // string to prefix user inputs with                             // NOLINT
     std::string input_suffix         = ""; // string to suffix user inputs with                             // NOLINT
-    std::string lookup_cache_static  = ""; // path of static ngram cache file for lookup decoding           // NOLINT
-    std::string lookup_cache_dynamic = ""; // path of dynamic ngram cache file for lookup decoding          // NOLINT
     std::string logits_file          = ""; // file for saving *all* logits                                  // NOLINT
 
     // llama-debug specific options
@@ -575,10 +613,6 @@ struct common_params {
     // return false from callback to abort model loading or true to continue
     llama_progress_callback load_progress_callback = NULL;
     void *                  load_progress_callback_user_data = NULL;
-
-    bool has_speculative() const {
-        return !speculative.model.path.empty() || !speculative.model.hf_repo.empty();
-    }
 };
 
 // call once at the start of a program if it uses libcommon
@@ -714,8 +748,6 @@ struct common_init_result {
 
     std::vector<llama_adapter_lora_ptr> & lora();
 
-    void free_context();
-
 private:
     struct impl;
     std::unique_ptr<impl> pimpl;

common/debug.cpp

@@ -45,6 +45,8 @@ static float common_ggml_get_float_value(const uint8_t * data,
     return v;
 }
 
+#define INDENT "    "
+
 template <bool abort>
 void common_debug_print_tensor(uint8_t * data, ggml_type type, const int64_t * ne, const size_t * nb, int64_t n) {
     GGML_ASSERT(n > 0);
@@ -60,41 +62,41 @@ void common_debug_print_tensor(uint8_t * data, ggml_type type, const int64_t * n
         }
     }
     for (int64_t i3 = 0; i3 < ne[3]; i3++) {
-        LOG_ERR("                                     [\n");
+        LOG(INDENT "[\n");
         for (int64_t i2 = 0; i2 < ne[2]; i2++) {
             if (i2 == n && ne[2] > 2 * n) {
-                LOG_ERR("                                      ..., \n");
+                LOG(INDENT INDENT "..., \n");
                 i2 = ne[2] - n;
             }
-            LOG_ERR("                                      [\n");
+            LOG(INDENT INDENT "[\n");
             for (int64_t i1 = 0; i1 < ne[1]; i1++) {
                 if (i1 == n && ne[1] > 2 * n) {
-                    LOG_ERR("                                       ..., \n");
+                    LOG(INDENT INDENT INDENT "..., \n");
                     i1 = ne[1] - n;
                 }
-                LOG_ERR("                                       [");
+                LOG(INDENT INDENT INDENT "[");
                 for (int64_t i0 = 0; i0 < ne[0]; i0++) {
                     if (i0 == n && ne[0] > 2 * n) {
-                        LOG_ERR("..., ");
+                        LOG("   ..., ");
                         i0 = ne[0] - n;
                     }
                     const float v = common_ggml_get_float_value(data, type, nb, i0, i1, i2, i3);
-                    LOG_ERR("%12.4f", v);
+                    LOG("%12.4f", v);
                     if (i0 < ne[0] - 1) {
-                        LOG_ERR(", ");
+                        LOG(", ");
                     }
                 }
-                LOG_ERR("],\n");
+                LOG("  ],\n");
             }
-            LOG_ERR("                                      ],\n");
+            LOG(INDENT INDENT "],\n");
         }
-        LOG_ERR("                                     ]\n");
-        LOG_ERR("                                     sum = %f\n", sum);
+        LOG(INDENT "]\n");
+        LOG(INDENT "sum = %f\n", sum);
     }
 
     if constexpr (abort) {
         if (std::isnan(sum)) {
-            LOG_ERR("encountered NaN - aborting\n");
+            LOG("encountered NaN - aborting\n");
             exit(0);
         }
     }
@@ -137,9 +139,9 @@ template <bool abort_on_nan> bool common_debug_cb_eval(struct ggml_tensor * t, b
     }
 
     if (matches_filter) {
-        LOG_ERR("%s: %24s = (%s) %10s(%s{%s}, %s}) = {%s}\n", __func__, t->name, ggml_type_name(t->type),
-                ggml_op_desc(t), src0->name, common_ggml_ne_string(src0).c_str(), src1 ? src1_str : "",
-                common_ggml_ne_string(t).c_str());
+        LOG("%s: %24s = (%s) %10s(%s{%s}, %s}) = {%s}\n", __func__, t->name, ggml_type_name(t->type),
+            ggml_op_desc(t), src0->name, common_ggml_ne_string(src0).c_str(), src1 ? src1_str : "",
+            common_ggml_ne_string(t).c_str());
     }
 
     const bool is_host = ggml_backend_buffer_is_host(t->buffer);

common/download.cpp

@@ -305,7 +305,10 @@ static bool common_pull_file(httplib::Client & cli,
     );
 
     if (!res) {
-        LOG_ERR("%s: error during download. Status: %d\n", __func__, res ? res->status : -1);
+        LOG_ERR("%s: download failed: %s (status: %d)\n",
+                __func__,
+                httplib::to_string(res.error()).c_str(),
+                res ? res->status : -1);
         return false;
     }
 

common/jinja/caps.cpp

@@ -63,7 +63,8 @@ static void caps_print_stats(value & v, const std::string & path) {
 
 std::map<std::string, bool> caps::to_map() const {
     return {
-        {"requires_typed_content", requires_typed_content},
+        {"supports_string_content", supports_string_content},
+        {"supports_typed_content", supports_typed_content},
         {"supports_tools", supports_tools},
         {"supports_tool_calls", supports_tool_calls},
         {"supports_parallel_tool_calls", supports_parallel_tool_calls},
@@ -89,7 +90,7 @@ caps caps_get(jinja::program & prog) {
         return v->stats.ops.find(op_name) != v->stats.ops.end();
     };
 
-    // case: typed content requirement
+    // case: typed content support
     caps_try_execute(
         prog,
         [&]() {
@@ -105,12 +106,16 @@ caps caps_get(jinja::program & prog) {
             // tools
             return json{nullptr};
         },
-        [&](bool, value & messages, value &) {
+        [&](bool success, value & messages, value &) {
             auto & content = messages->at(0)->at("content");
             caps_print_stats(content, "messages[0].content");
             if (has_op(content, "selectattr") || has_op(content, "array_access")) {
                 // accessed as an array
-                result.requires_typed_content = true;
+                result.supports_typed_content = true;
+            }
+            if (!success) {
+                // failed to execute with content as string
+                result.supports_string_content = false;
             }
         }
     );

common/jinja/caps.h

@@ -14,7 +14,9 @@ struct caps {
     bool supports_parallel_tool_calls = true;
     bool supports_preserve_reasoning = false; // support assistant message with reasoning_content
 
-    bool requires_typed_content = false; // default: use string content
+    // one of the 2 content capabilities must be true
+    bool supports_string_content = true;
+    bool supports_typed_content = false;
 
     // for reporting on server
     std::map<std::string, bool> to_map() const;

common/jinja/runtime.cpp

@@ -144,6 +144,13 @@ value binary_expression::execute_impl(context & ctx) {
         return false;
     };
 
+    auto test_is_in = [&]() -> bool {
+        func_args args(ctx);
+        args.push_back(left_val);
+        args.push_back(right_val);
+        return global_builtins().at("test_is_in")(args)->as_bool();
+    };
+
     // Handle undefined and null values
     if (is_val<value_undefined>(left_val) || is_val<value_undefined>(right_val)) {
         if (is_val<value_undefined>(right_val) && (op.value == "in" || op.value == "not in")) {
@@ -223,19 +230,11 @@ value binary_expression::execute_impl(context & ctx) {
             return result;
         }
     } else if (is_val<value_array>(right_val)) {
-        auto & arr = right_val->as_array();
-        bool member = false;
-        for (const auto & item : arr) {
-            if (*left_val == *item) {
-                member = true;
-                break;
-            }
-        }
+        // case: 1 in [0, 1, 2]
+        bool member = test_is_in();
         if (op.value == "in") {
-            JJ_DEBUG("Checking membership: %s in Array is %d", left_val->type().c_str(), member);
             return mk_val<value_bool>(member);
         } else if (op.value == "not in") {
-            JJ_DEBUG("Checking non-membership: %s not in Array is %d", left_val->type().c_str(), !member);
             return mk_val<value_bool>(!member);
         }
     }
@@ -252,22 +251,23 @@ value binary_expression::execute_impl(context & ctx) {
 
     // String membership
     if (is_val<value_string>(left_val) && is_val<value_string>(right_val)) {
-        auto left_str = left_val->as_string().str();
-        auto right_str = right_val->as_string().str();
+        // case: "a" in "abc"
+        bool member = test_is_in();
         if (op.value == "in") {
-            return mk_val<value_bool>(right_str.find(left_str) != std::string::npos);
+            return mk_val<value_bool>(member);
         } else if (op.value == "not in") {
-            return mk_val<value_bool>(right_str.find(left_str) == std::string::npos);
+            return mk_val<value_bool>(!member);
         }
     }
 
     // Value key in object
     if (is_val<value_object>(right_val)) {
-        bool has_key = right_val->has_key(left_val);
+        // case: key in {key: value}
+        bool member = test_is_in();
         if (op.value == "in") {
-            return mk_val<value_bool>(has_key);
+            return mk_val<value_bool>(member);
         } else if (op.value == "not in") {
-            return mk_val<value_bool>(!has_key);
+            return mk_val<value_bool>(!member);
         }
     }
 
@@ -446,6 +446,12 @@ value for_statement::execute_impl(context & ctx) {
 
     value iterable_val = iter_expr->execute(scope);
 
+    // mark the variable being iterated as used for stats
+    if (ctx.is_get_stats) {
+        iterable_val->stats.used = true;
+        iterable_val->stats.ops.insert("array_access");
+    }
+
     if (iterable_val->is_undefined()) {
         JJ_DEBUG("%s", "For loop iterable is undefined, skipping loop");
         iterable_val = mk_val<value_array>();

common/jinja/value.cpp

@@ -114,6 +114,18 @@ static T slice(const T & array, int64_t start, int64_t stop, int64_t step = 1) {
     return result;
 }
 
+template<typename T>
+static value empty_value_fn(const func_args &) {
+    if constexpr (std::is_same_v<T, value_int>) {
+        return mk_val<T>(0);
+    } else if constexpr (std::is_same_v<T, value_float>) {
+        return mk_val<T>(0.0);
+    } else if constexpr (std::is_same_v<T, value_bool>) {
+        return mk_val<T>(false);
+    } else {
+        return mk_val<T>();
+    }
+}
 template<typename T>
 static value test_type_fn(const func_args & args) {
     args.ensure_count(1);
@@ -128,6 +140,13 @@ static value test_type_fn(const func_args & args) {
     JJ_DEBUG("test_type_fn: type=%s or %s result=%d", typeid(T).name(), typeid(U).name(), is_type ? 1 : 0);
     return mk_val<value_bool>(is_type);
 }
+template<typename T, typename U, typename V>
+static value test_type_fn(const func_args & args) {
+    args.ensure_count(1);
+    bool is_type = is_val<T>(args.get_pos(0)) || is_val<U>(args.get_pos(0)) || is_val<V>(args.get_pos(0));
+    JJ_DEBUG("test_type_fn: type=%s, %s or %s result=%d", typeid(T).name(), typeid(U).name(), typeid(V).name(), is_type ? 1 : 0);
+    return mk_val<value_bool>(is_type);
+}
 template<value_compare_op op>
 static value test_compare_fn(const func_args & args) {
     args.ensure_count(2, 2);
@@ -347,8 +366,8 @@ const func_builtins & global_builtins() {
         {"test_is_integer", test_type_fn<value_int>},
         {"test_is_float", test_type_fn<value_float>},
         {"test_is_number", test_type_fn<value_int, value_float>},
-        {"test_is_iterable", test_type_fn<value_array, value_string>},
-        {"test_is_sequence", test_type_fn<value_array, value_string>},
+        {"test_is_iterable", test_type_fn<value_array, value_string, value_undefined>},
+        {"test_is_sequence", test_type_fn<value_array, value_string, value_undefined>},
         {"test_is_mapping", test_type_fn<value_object>},
         {"test_is_lower", [](const func_args & args) -> value {
             args.ensure_vals<value_string>();
@@ -374,6 +393,33 @@ const func_builtins & global_builtins() {
         {"test_is_lt", test_compare_fn<value_compare_op::lt>},
         {"test_is_lessthan", test_compare_fn<value_compare_op::lt>},
         {"test_is_ne", test_compare_fn<value_compare_op::ne>},
+        {"test_is_in", [](const func_args & args) -> value {
+            args.ensure_count(2);
+            auto needle   = args.get_pos(0);
+            auto haystack = args.get_pos(1);
+            if (is_val<value_undefined>(haystack)) {
+                return mk_val<value_bool>(false);
+            }
+            if (is_val<value_array>(haystack)) {
+                for (const auto & item : haystack->as_array()) {
+                    if (*needle == *item) {
+                        return mk_val<value_bool>(true);
+                    }
+                }
+                return mk_val<value_bool>(false);
+            }
+            if (is_val<value_string>(haystack)) {
+                if (!is_val<value_string>(needle)) {
+                    throw raised_exception("'in' test expects args[1] as string when args[0] is string, got args[1] as " + needle->type());
+                }
+                return mk_val<value_bool>(
+                    haystack->as_string().str().find(needle->as_string().str()) != std::string::npos);
+            }
+            if (is_val<value_object>(haystack)) {
+                return mk_val<value_bool>(haystack->has_key(needle));
+            }
+            throw raised_exception("'in' test expects iterable as first argument, got " + haystack->type());
+        }},
         {"test_is_test", [](const func_args & args) -> value {
             args.ensure_vals<value_string>();
             auto & builtins = global_builtins();
@@ -1003,7 +1049,22 @@ const func_builtins & value_none_t::get_builtins() const {
     static const func_builtins builtins = {
         {"default", default_value},
         {"tojson", tojson},
-        {"string", [](const func_args &) -> value { return mk_val<value_string>("None"); }}
+        {"string", [](const func_args &) -> value {
+            return mk_val<value_string>("None");
+        }},
+        {"safe", [](const func_args &) -> value {
+            return mk_val<value_string>("None");
+        }},
+        {"strip", [](const func_args &) -> value {
+            return mk_val<value_string>("None");
+        }},
+        {"items", empty_value_fn<value_array>},
+        {"map", empty_value_fn<value_array>},
+        {"reject", empty_value_fn<value_array>},
+        {"rejectattr", empty_value_fn<value_array>},
+        {"select", empty_value_fn<value_array>},
+        {"selectattr", empty_value_fn<value_array>},
+        {"unique", empty_value_fn<value_array>},
     };
     return builtins;
 }
@@ -1012,10 +1073,33 @@ const func_builtins & value_none_t::get_builtins() const {
 const func_builtins & value_undefined_t::get_builtins() const {
     static const func_builtins builtins = {
         {"default", default_value},
-        {"tojson", [](const func_args & args) -> value {
-            args.ensure_vals<value_undefined>();
-            return mk_val<value_string>("null");
-        }},
+        {"capitalize", empty_value_fn<value_string>},
+        {"first", empty_value_fn<value_undefined>},
+        {"items", empty_value_fn<value_array>},
+        {"join", empty_value_fn<value_string>},
+        {"last", empty_value_fn<value_undefined>},
+        {"length", empty_value_fn<value_int>},
+        {"list", empty_value_fn<value_array>},
+        {"lower", empty_value_fn<value_string>},
+        {"map", empty_value_fn<value_array>},
+        {"max", empty_value_fn<value_undefined>},
+        {"min", empty_value_fn<value_undefined>},
+        {"reject", empty_value_fn<value_array>},
+        {"rejectattr", empty_value_fn<value_array>},
+        {"replace", empty_value_fn<value_string>},
+        {"reverse", empty_value_fn<value_array>},
+        {"safe", empty_value_fn<value_string>},
+        {"select", empty_value_fn<value_array>},
+        {"selectattr", empty_value_fn<value_array>},
+        {"sort", empty_value_fn<value_array>},
+        {"string", empty_value_fn<value_string>},
+        {"strip", empty_value_fn<value_string>},
+        {"sum", empty_value_fn<value_int>},
+        {"title", empty_value_fn<value_string>},
+        {"truncate", empty_value_fn<value_string>},
+        {"unique", empty_value_fn<value_array>},
+        {"upper", empty_value_fn<value_string>},
+        {"wordcount", empty_value_fn<value_int>},
     };
     return builtins;
 }

common/jinja/value.h

@@ -12,6 +12,7 @@
 #include <set>
 #include <sstream>
 #include <string>
+#include <unordered_map>
 #include <vector>
 
 namespace jinja {

common/ngram-cache.cpp

@@ -192,12 +192,12 @@ void common_ngram_cache_draft(
             break;
         }
 
-        LOG(" - draft candidate: token=%d\n", drafted_token);
+        LOG_DBG(" - draft candidate: token=%d\n", drafted_token);
         draft.push_back(drafted_token);
     }
 }
 
-void common_ngram_cache_save(common_ngram_cache & ngram_cache, std::string & filename) {
+void common_ngram_cache_save(common_ngram_cache & ngram_cache, const std::string & filename) {
     std::ofstream file_out(filename, std::ios::binary);
     for (std::pair<common_ngram, common_ngram_cache_part> item : ngram_cache) {
         const common_ngram      ngram        = item.first;
@@ -217,10 +217,9 @@ void common_ngram_cache_save(common_ngram_cache & ngram_cache, std::string & fil
             file_out.write(reinterpret_cast<const char *>(&count), sizeof(int32_t));
         }
     }
-
 }
 
-common_ngram_cache common_ngram_cache_load(std::string & filename) {
+common_ngram_cache common_ngram_cache_load(const std::string & filename) {
     std::ifstream hashmap_file(filename, std::ios::binary);
     if (!hashmap_file) {
         throw std::ifstream::failure("Unable to open file " + filename);

common/ngram-cache.h

@@ -88,12 +88,12 @@ void common_ngram_cache_draft(
 // Save an ngram cache to a file.
 // ngram_cache: the ngram cache to save.
 // filename:    the path under which to save the ngram cache.
-void common_ngram_cache_save(common_ngram_cache & ngram_cache, std::string & filename);
+void common_ngram_cache_save(common_ngram_cache & ngram_cache, const std::string & filename);
 
 // Load an ngram cache saved with common_ngram_cache_save.
 // filename: the path from which to load the ngram cache.
 // returns:  an ngram cache containing the information saved to filename.
-common_ngram_cache common_ngram_cache_load(std::string & filename);
+common_ngram_cache common_ngram_cache_load(const std::string & filename);
 
 // Merge two ngram caches.
 // ngram_cache_target: the ngram cache to which to add the information from ngram_cache_add.

common/ngram-map.cpp

@@ -0,0 +1,530 @@
+#include "common.h"
+#include "log.h"
+#include "ngram-map.h"
+
+#include <cinttypes>
+#include <cstdint>
+#include <cstdio>
+#include <sstream>
+
+// prime number used for LCG hash function (32 bit), it is near (sqrt(5) - 1)/2 * 2^32.
+#define LCG_FACTOR 2654435761UL
+
+// Compute the LCG hash of a n-gram of size len at offset start.
+static uint32_t common_ngram_map_hash(const llama_tokens & tokens, size_t start, size_t len) {
+    uint32_t hash = 0;
+    for (size_t i = 0; i < len; ++i) {
+        hash = hash * LCG_FACTOR + tokens[start + i];
+    }
+    return hash;
+}
+
+// Print the values of a sublist of `llama_tokens & inp` to a string in the form [v0, v1, v2, ...].
+static std::string common_tokens_to_str(const llama_tokens & inp, size_t start, size_t length) {
+    std::ostringstream oss;
+    oss << '[';
+    for (size_t i = 0; i < length; ++i) {
+        if (i > 0) {
+            oss << ", ";
+        }
+        oss << inp[start + i];
+    }
+    oss << ']';
+    return oss.str();
+}
+
+
+// n-gram simple
+//
+
+/**
+ * Perform speculative generation using the model's own token history.
+ * Searches for a matching pattern in the token history and returns draft tokens.
+ *
+ * @param state     Current state of this implementation
+ * @param tokens    Token history to search in
+ * @param sampled   Last sampled token
+ * @return Vector of draft tokens, empty if no matching pattern is found
+ */
+llama_tokens common_ngram_simple_draft(
+        const common_ngram_simple_config & config,
+        const llama_tokens & tokens, llama_token sampled) {
+
+    // Simple implementation of self-speculative decoding without a draft model.
+    //
+    const size_t cur_len = tokens.size();
+
+    const size_t n_draft_min = config.size_ngram; // size of n-gram to lookup in token history
+    const size_t n_draft_max = config.size_mgram; // the m-gram following the found n-gram is used for draft
+
+    // vector for tokens we want to verify.
+    // return empty vector if there is no match.
+    llama_tokens draft_tokens;
+
+    // We need at least n_draft_min + n_draft_max + 1 tokens.
+    if (cur_len <= static_cast<size_t>(n_draft_min + n_draft_max + 1)) {
+        return draft_tokens;
+    }
+
+    // pattern search
+    llama_tokens pattern;
+    pattern.reserve(n_draft_min);
+    for (size_t j = cur_len - n_draft_min + 1; j < cur_len; ++j) {
+        pattern.push_back(tokens[j]);
+    }
+    pattern.push_back(sampled); // add the last token to the pattern
+
+    size_t match_pos = 0; // we ignore position 0, position 0 == no match
+                          // search backwards, but skip the current match (we are currently there)
+    for (size_t j = cur_len - n_draft_min - 1; j > 0; --j) {
+        bool match = true;
+        for (size_t k = 0; k < pattern.size(); ++k) {
+            if (tokens[j + k] != pattern[k]) {
+                match = false;
+                break;
+            }
+        }
+        if (match) {
+            match_pos = j;
+            break;
+        }
+    }
+    if (match_pos == 0) {
+        return draft_tokens;
+    }
+
+    const size_t copy_max = std::min(
+            n_draft_max,
+            cur_len - (match_pos + n_draft_min)
+            );
+    if (copy_max < n_draft_min) {
+        return draft_tokens;
+    }
+    LOG_DBG("%s: #tokens = %zu: found matching pattern at pos %zu, length %zu, draft length %zu\n",
+            __func__, cur_len,
+            match_pos, pattern.size(), copy_max);
+
+    draft_tokens.reserve(copy_max);
+    for (size_t j = 0; j < copy_max; ++j) {
+        draft_tokens.push_back(tokens[match_pos + n_draft_min + j]);
+    }
+    return draft_tokens;
+}
+
+
+// n-gram map
+//
+
+// maximum number of counted values of a ngram map value.
+#define COMMON_NGRAM_MAX_VALUE_COUNT 16380
+
+void common_ngram_map_begin(
+    common_ngram_map & map, const llama_tokens & tokens) {
+    size_t size_begin = tokens.size();
+
+    LOG_DBG("%s: begin, idx_last_draft=%zu, new begin=%zu, #keys=%zu\n", __func__,
+            map.idx_last_check, size_begin, map.keys.size());
+
+    size_t count_map_entries_upd = 0;
+    if (!map.key_map.empty() && size_begin < map.idx_last_check) {
+        if (map.show_key_map_stats) {
+            // Print statistics of hash map map_key.
+            size_t count_nonzero = 0;
+            uint32_t min_idx = UINT32_MAX;
+            uint32_t max_idx = 0;
+            for (size_t i = 0; i < map.key_map.size(); ++i) {
+                uint32_t key_idx = map.key_map[i];
+                if (key_idx != 0) {
+                    ++count_nonzero;
+                    if (key_idx < min_idx) min_idx = key_idx;
+                    if (key_idx > max_idx) max_idx = key_idx;
+                }
+            }
+            if (count_nonzero == 0) {
+                min_idx = 0;
+            }
+            LOG_INF("%s: key_map stats: entries=%zu, min_idx=%u, max_idx=%u, key_map_last_idx=%u\n",
+                    __func__, count_nonzero, min_idx, max_idx, map.key_map_last_idx);
+        }
+
+        // Update the map from hash to key index (clear outdated entries).
+        for (size_t i = 0; i < map.key_map.size(); ++i) {
+            uint32_t key_idx = map.key_map[i];
+            if (key_idx >= map.size_last_begin) {
+                map.key_map[i] = 0;
+                count_map_entries_upd++;
+            }
+        }
+        map.key_map_last_idx = (map.size_last_begin > 0) ? map.size_last_begin - 1 : 0;
+    }
+
+    if (size_begin < map.idx_last_check && !map.keys.empty()) {
+        // The next token generation will start at index size_begin.
+        // The tokens between map.size_last_begin and size_begin are no longer valid.
+        //
+        // Refresh map: Remove all entries with index >= map.size_last_begin.
+        size_t count_keys = map.keys.size();
+        size_t count_keys_del = 0;
+        size_t count_values_del = 0;
+        for (int32_t i = map.keys.size() - 1; i >= 0; --i) {
+            common_ngram_map_key & key = map.keys[i];
+            if (key.key_idx >= map.size_last_begin) {
+                // Delete the key.
+                LOG_DBG("%s: delete key %d at index %zu (>= size_last_begin=%zu)\n", __func__, i, key.key_idx, map.size_last_begin);
+                map.keys.erase(map.keys.begin() + i);
+                count_keys_del++;
+                continue;
+            }
+            if (map.key_only) {
+                continue;
+            }
+
+            // Check the indices of the values.
+            for (int16_t j = COMMON_NGRAM_MAX_VALUES - 1; j >= 0; --j) {
+                common_ngram_map_value & value = key.values[j];
+                if (value.value_idx >= map.size_last_begin) {
+                    // Delete the value.
+                    count_values_del++;
+
+                    // Move all values after this value to the left.
+                    for (uint16_t k = j; k < COMMON_NGRAM_MAX_VALUES - 1; ++k) {
+                        key.values[k] = key.values[k + 1];
+                    }
+                    // Clear the last value.
+                    key.values[COMMON_NGRAM_MAX_VALUES - 1].value_idx = 0;
+                    key.values[COMMON_NGRAM_MAX_VALUES - 1].value_num = 0;
+                }
+            }
+            if (key.values[0].value_idx == 0) {
+                // No values left, delete the key.
+                LOG_DBG("%s: delete key %d at index %zu (no values left)\n", __func__, i, key.key_idx);
+                map.keys.erase(map.keys.begin() + i);
+                count_keys_del++;
+            }
+        }
+
+        LOG_INF("%s: refresh map: idx_last_draft=%zu, new begin=%zu, #keys_checked=%zu, #keys_del=%zu, #values_del=%zu, #hashes_upd=%zu\n", __func__,
+                map.idx_last_check, size_begin,
+                count_keys, count_keys_del, count_values_del, count_map_entries_upd);
+    }
+
+    map.idx_last_check = (map.size_last_begin > 0) ? map.size_last_begin - 1 : 0;
+    map.size_last_begin = size_begin;
+}
+
+void common_ngram_map_draft(common_ngram_map & map,
+        const llama_tokens & inp, llama_token sampled,
+        llama_tokens & draft) {
+    // reset last key and value.
+    map.last_draft_created   = false;
+    map.last_draft_key_idx   = 0;
+    map.last_draft_value_idx = 0;
+
+    const size_t cur_len = inp.size();
+    const uint16_t n = map.size_key;
+    const uint16_t m = map.size_value;
+    if (cur_len < static_cast<size_t>(2 * n + m)) {
+        return;
+    }
+    if (cur_len >= static_cast<size_t>(UINT32_MAX)) {
+        // key_map uses uint32_t instead of size_t.
+        GGML_ABORT("%s: cur_len exceeds UINT32_MAX: %zu", __func__, cur_len);
+    }
+
+    if (map.idx_last_check  > cur_len) {
+        // Should not happen because of common_ngram_map_begin().
+        GGML_ABORT("%s: map.idx_last_check > cur_len: %zu > %zu", __func__, map.idx_last_check, cur_len);
+    }
+    map.idx_last_check = cur_len;
+
+    // search pattern, the key n-gram
+    std::vector<llama_token> key_tokens;
+    key_tokens.reserve(n);
+    for (size_t j = cur_len - n + 1; j < cur_len; ++j) {
+        key_tokens.push_back(inp[j]);
+    }
+    key_tokens.push_back(sampled);
+
+    // search for the key in the map
+    size_t match_pos = 0;
+    if (map.size_last_begin > cur_len) {
+        GGML_ABORT("%s: map.size_last_begin > cur_len: %zu > %zu", __func__, map.size_last_begin, cur_len);
+    }
+    if (!map.key_map.empty()) {
+        // Search for the key in the map key_map from hash of ngrams to index of ngram.
+        uint32_t idx_hash = (common_ngram_map_hash(key_tokens, 0, n) % map.key_map.size());
+        uint32_t idx_key = map.key_map[idx_hash];
+        if (idx_key != 0 && idx_key < cur_len - n - m - 1) {
+            // Check if the key matches the key at idx_key (because of possible collisions).
+            bool match = true;
+            for (size_t k = 0; k < n; ++k) {
+                if (inp[idx_key + k] != key_tokens[k]) {
+                    match = false;
+                    break;
+                }
+            }
+            LOG_DBG("%s: key hash %x -> idx_key %d: match %d\n", __func__, idx_hash, idx_key, match ? 1 : 0);
+            if (match) {
+                match_pos = idx_key;
+            }
+        }
+    }
+    if (match_pos == 0 && map.size_last_begin > (size_t) (n + m + 1)) {
+        // Search for the key in [1, map.size_last_begin - n - m -1], descending.
+        for (size_t j = map.size_last_begin - n - m - 1; j > map.key_map_last_idx; --j) {
+            // Check if the key matches the key.
+            bool match = true;
+            for (size_t k = 0; k < n; ++k) {
+                if (inp[j + k] != key_tokens[k]) {
+                    match = false;
+                    break;
+                }
+            }
+            if (match) {
+               match_pos = j;
+               break;
+            }
+        }
+    }
+    if (match_pos == 0) {
+        // In case of a reasoning chat, the part after size_last_begin may be deleted/reordered later.
+        //
+        // Search in [size_last_begin, cur_len - n - m - 1], descending.
+        for (size_t j = cur_len - n - m - 1; j > map.size_last_begin && j > map.key_map_last_idx; --j) {
+            bool match = true;
+            for (size_t k = 0; k < n; ++k) {
+                if (inp[j + k] != key_tokens[k]) {
+                    match = false;
+                    break;
+                }
+            }
+            if (match) {
+               match_pos = j;
+               break;
+            }
+        }
+    }
+    if (match_pos > 0) {
+        LOG_DBG("%s: cur_len = %zu, n = %d, m = %d, sz_tkns = %zu, sampled = %d, match_pos = %zu\n", __func__,
+            cur_len, n, m, key_tokens.size(), sampled, match_pos);
+    }
+
+    if (!map.key_map.empty()) {
+        // Add hashes of new ngrams in key_map.
+        //
+        // Use the same order as above.
+        if (map.size_last_begin > (size_t) (n + m + 1)) {
+            for (size_t j = map.size_last_begin - n - m - 1; j > map.key_map_last_idx; --j) {
+                // compute hash and store index of ngram at idx j in the map.
+                uint32_t idx_hash = (common_ngram_map_hash(inp, j, n) % map.key_map.size());
+                if (map.key_map[idx_hash] == 0) {
+                    map.key_map[idx_hash] = j; // collisions may occur
+                }
+            }
+        }
+
+        for (size_t j = cur_len - n - m - 1; j > map.size_last_begin && j > map.key_map_last_idx; --j) {
+            // compute hash and store index of ngram at idx j in the map.
+            uint32_t idx_hash = (common_ngram_map_hash(inp, j, n) % map.key_map.size());
+            if (map.key_map[idx_hash] == 0) {
+                map.key_map[idx_hash] = j;
+            }
+        }
+        map.key_map_last_idx = std::max(static_cast<uint32_t>(cur_len - n - m - 1), map.key_map_last_idx);
+    }
+
+    if (match_pos == 0) {
+        return;
+    }
+
+    // We have a match, now we look for the statistics of the key.
+    size_t key_offset = map.keys.size(); // offset in the map
+    // We iterate through the std::vector<common_ngram_map_key> map->keys.
+    for (size_t i = 0; i < map.keys.size(); ++i) {
+        bool match = true;
+        for (size_t j = 0; j < n; ++j) {
+            if (inp[map.keys[i].key_idx + j] != key_tokens[j]) {
+                match = false;
+                break;
+            }
+        }
+        if (match) {
+            key_offset = i;
+            break;
+        }
+    }
+    if (key_offset == map.keys.size()) {
+        // We create a new key-entry, it will get offset key_offset.
+        common_ngram_map_key new_key;
+        new_key.key_idx = match_pos;
+        new_key.stat_idx = 0;
+        new_key.key_num = 0;
+        for (int i = 0; i < COMMON_NGRAM_MAX_VALUES; ++i) {
+            new_key.values[i].value_num = 0;
+            new_key.values[i].n_accepted = m;
+        }
+        map.keys.push_back(new_key);
+    }
+
+    // our key n-gram:
+    common_ngram_map_key & curr_key = map.keys[key_offset];
+
+    // update number of key hits
+    curr_key.key_num = (uint16_t) std::min((int) map.keys[key_offset].key_num + 1,
+            (int) COMMON_NGRAM_MAX_VALUE_COUNT);
+
+    if (map.key_only) {
+        // simple mode:
+        // Fill in the draft with the m tokens following the key.
+        // We work with value values[0] only.
+        int n_draft_tokens = std::min((int) m, (int) curr_key.values[0].n_accepted);
+
+        for (int i = 0; i < n_draft_tokens; ++i) {
+            draft.push_back(inp[match_pos + n + i]);
+        }
+
+        LOG_DBG("%s: key_idx = %zu, key_offset = %zu, key_num = %d, draft.size = %zu\n", __func__,
+                curr_key.key_idx, key_offset, curr_key.key_num, draft.size());
+
+        map.last_draft_created   = false;
+        map.last_draft_key_idx   = key_offset;
+        map.last_draft_value_idx = 0; // value 0 is used for simple mode
+        return;
+    }
+
+    if (curr_key.key_num < map.min_hits) {
+        // not enough hits to consider this a good draft
+        LOG_DBG("%s: key_offset = %zu, key_num = %d, min_hits = %d, no draft\n", __func__,
+                key_offset, curr_key.key_num, map.min_hits);
+        return;
+    }
+
+    // complex mode: examine the different m-grams after this key n-gram.
+    //
+
+    // determine all (max COMMON_NGRAM_MAX_VALUES) m-grams after the key n-gram.
+    for (size_t i = curr_key.stat_idx; i <= match_pos; ++i) {
+        // begins the key n-gram at index i?
+        bool match_key = true;
+        for (size_t k = 0; k < n; ++k) {
+            if (inp[i + k] != key_tokens[k]) {
+                match_key = false;
+                break;
+            }
+        }
+        if (!match_key) {
+            continue;
+        }
+
+        // Do we haven a existing value m-gram or a new one after the key at index i?
+        size_t idx_begin_value_key = i + n;
+        int idx_value = -1;
+        for (int v = 0; v < COMMON_NGRAM_MAX_VALUES; ++v) {
+            size_t idx_begin_value_v = curr_key.values[v].value_idx;
+            if (idx_begin_value_v == 0) {
+                // We found an empty value slot => we found a new value m-gram after the key n-gram.
+                curr_key.values[v].value_idx = idx_begin_value_key;
+                curr_key.values[v].value_num = 0;
+                curr_key.values[v].n_accepted = m;
+                idx_value = v;
+                break;
+            }
+            bool match = true;
+            for (size_t j = 0; j < m; ++j) {
+                if (inp[idx_begin_value_key + j] != inp[idx_begin_value_v + j]) {
+                    match = false;
+                    break;
+                }
+            }
+            if (match) {
+                // We found an existing value m-gram after the key n-gram.
+                idx_value = v;
+                break;
+            }
+        }
+        if (idx_value >= 0) {
+            // We found a value m-gram of the key n-gram.
+            curr_key.values[idx_value].value_num = (uint16_t) std::min((int) curr_key.values[idx_value].value_num + 1,
+                    (int) COMMON_NGRAM_MAX_VALUE_COUNT);
+        }
+    }
+    // the statistics are updated up to match_pos.
+    curr_key.stat_idx = match_pos;
+
+    // Do we have a value we could use for the draft?
+    uint16_t max_occur = 0;
+    int slot_max = 0;
+    for (int v = 0; v < COMMON_NGRAM_MAX_VALUES; ++v) {
+        uint16_t curr_occur = curr_key.values[v].value_num;
+        if (curr_occur > max_occur) {
+            max_occur = curr_occur;
+            slot_max = v;
+        }
+    }
+    // What is sum of the other occurrences?
+    uint32_t sum_occur = 0;
+    for (int v = 0; v < COMMON_NGRAM_MAX_VALUES; ++v) {
+        if (v == slot_max) {
+            continue;
+        }
+        uint16_t curr_occur = curr_key.values[v].value_num;
+        sum_occur += curr_occur;
+    }
+
+    LOG_INF("%s: key_offset = %zu, max_occur = %d, sum_occur = %d, slot_max = %d [%zu/%d, %zu/%d, %zu/%d, %zu/%d]\n", __func__,
+            key_offset,
+            max_occur, sum_occur, slot_max,
+            curr_key.values[0].value_idx, curr_key.values[0].value_num,
+            curr_key.values[1].value_idx, curr_key.values[1].value_num,
+            curr_key.values[2].value_idx, curr_key.values[2].value_num,
+            curr_key.values[3].value_idx, curr_key.values[3].value_num
+        );
+    // Print the tokens of the four values (if idx != 0), use LOG_INF
+    for (int v = 0; v < COMMON_NGRAM_MAX_VALUES; ++v) {
+        if (curr_key.values[v].value_idx != 0) {
+            LOG_INF("%s: value[%d] = %s\n", __func__, v, common_tokens_to_str(inp, curr_key.values[v].value_idx, m).c_str());
+        }
+    }
+
+    if (sum_occur > 0 && max_occur < 2 * sum_occur) {
+        // The most frequent value is not much more frequent than the other values.
+        // We do not use the draft.
+        return;
+    }
+
+    // We use the most frequent value values[slot_max] for the draft.
+    // Fill in the draft with the m tokens following the key.
+    int n_draft_tokens = std::min((int) m, (int) curr_key.values[slot_max].n_accepted);
+
+    for (int i = 0; i < n_draft_tokens; ++i) {
+        draft.push_back(inp[match_pos + n + i]);
+    }
+
+    LOG_INF("%s: key_offset = %zu, slot_max = %d, key_num = %d, draft.size = %zu\n", __func__,
+            key_offset, slot_max,
+            curr_key.key_num, draft.size());
+
+    map.last_draft_created   = true;
+    map.last_draft_key_idx   = key_offset;
+    map.last_draft_value_idx = slot_max; // value used for draft generation.
+}
+
+void common_ngram_map_accept(common_ngram_map & map, uint16_t n_accepted) {
+    if (!map.last_draft_created) {
+        return;
+    }
+
+    // find the key and its chosen value.
+    const size_t key_idx = map.last_draft_key_idx;
+    const size_t val_idx = map.last_draft_value_idx;
+
+    // find key corresponding to key_idx.
+    common_ngram_map_key & curr_key = map.keys[key_idx];
+    // find value corresponding to val_idx.
+    struct common_ngram_map_value & curr_value = curr_key.values[val_idx]; // value used for draft generation.
+
+    // update the value statistics
+    LOG_INF("common_ngram_map_send_accepted: n_accepted = %d, prev value_num = %d\n",
+            n_accepted, curr_value.n_accepted);
+    curr_value.n_accepted = n_accepted;
+}

common/ngram-map.h

@@ -0,0 +1,115 @@
+#pragma once
+//
+// common/ngram-map.h: structures used to manage a map from n-grams to a list of m-grams
+//
+// These structures are used to do a lookup of n-grams followed by m-grams in token history.
+//
+// There are two algorithms implemented:
+// 1. ngram_simple: lookup of n-grams followed by m-grams in token history.
+// 2. ngram_map: lookup of n-grams followed by m-grams in token history using a map.
+//    The map is a vector of key n-grams, and for each key n-gram there is a list of value m-grams.
+//
+// ref: https://github.com/ggml-org/llama.cpp/pull/18471
+//
+
+#include "llama.h"
+#include "common.h"
+
+#include <vector>
+
+// n-gram simple
+//
+
+// config of n-gram simple.
+struct common_ngram_simple_config {
+    uint16_t   size_ngram;      // size of n-grams to lookup in self-mode
+    uint16_t   size_mgram;      // size of m-grams to draft in self-mode
+};
+
+// Searches for a n-gram in the history and checks whether a draft sequence should be generated.
+llama_tokens common_ngram_simple_draft(
+        const common_ngram_simple_config & config,
+        const llama_tokens & tokens, llama_token sampled);
+
+
+// n-gram map
+//
+
+// maximum number of m-gram values stored for each key n-gram.
+#define COMMON_NGRAM_MAX_VALUES 4
+
+// number of entries in the (optional, size 0 to disable) map from ngram-hash to ngram-index.
+#define COMMON_NGRAM_HASH_MAP_SIZE 262144
+
+// statistics of a m-gram after a known n-gram
+struct common_ngram_map_value {
+    size_t   value_idx =  0;  // index of value m-gram in token-history (0 if unused)
+    uint16_t value_num =  0;  // number of occurrences of this value m-gram after the key n-gram (0 in an unused values-slot)
+    int16_t n_accepted = -1;  // number of accepted tokens at last draft (-1 if unused)
+};
+
+// statistics of a n-gram
+struct common_ngram_map_key {
+    size_t   key_idx;   // index of key n-gram in token-history
+    size_t   stat_idx;  // index of last token of stastistics computation (key_num, values)
+
+    uint16_t key_num;   // number of occurrences of this key n-gram in token-history
+    common_ngram_map_value values[COMMON_NGRAM_MAX_VALUES]; // some known values after the key
+};
+
+// map from n-grams to following m-grams in token-history
+struct common_ngram_map {
+    uint16_t size_key;   // size of key n-grams
+    uint16_t size_value; // size of value m-grams
+
+    bool key_only;       // true if only key n-grams are used, no values.
+
+    std::vector<common_ngram_map_key> keys; // key n-grams which occur several times in token-history
+    uint16_t min_hits;   // minimum number of key hits to consider a draft
+
+    bool     show_key_map_stats = false; // true, if statistics of the key_map should be printed.
+
+    common_ngram_map(uint16_t sz_key, uint16_t sz_value, bool only_keys,
+                     uint16_t min_hits)
+        : size_key(sz_key), size_value(sz_value), key_only(only_keys),
+          min_hits(min_hits) {
+        key_map.resize(COMMON_NGRAM_HASH_MAP_SIZE); // 2^18 hash entries, 0 entries if key_map shouldn't be used
+    }
+
+    // In reasoning chats the previous reasoning block will be removed from context history.
+    // A rebuild of the ngram map is needed after that.
+
+    size_t   size_last_begin      = 0; // number of tokens at previous start of generation
+
+    bool     last_draft_created   = false; // true if a draft was created at last call.
+    size_t   last_draft_key_idx   = 0; // index of last key used for draft generation (0 = no draft)
+    uint16_t last_draft_value_idx = 0; // index of last value used for draft generation.
+
+    size_t   idx_last_check       = 0; // index of last check in context history
+
+    // optional map "hash to ngram-index" for faster lookup of n-grams. map is empty if unused.
+    //
+    // uint32_t instead of size_t (size of current histories is << UINT32_MAX)
+    std::vector<uint32_t> key_map;              // key_map[hash] = index of ngram in context window
+    uint32_t              key_map_last_idx = 0; // index of the last ngram added to key_map
+};
+
+// Initialize the n-gram map with the given token history.
+// map:                the ngram map to initialize.
+// tokens:             the token history to base the map on.
+void common_ngram_map_begin(
+    common_ngram_map & map,
+    const llama_tokens & tokens);
+
+// Searches for the n-gram in the history and checks whether a draft sequence should be generated.
+// map:                the ngram map to search in.
+// inp:                the tokens generated so far.
+// sampled:            the token that was just sampled.
+// draft:              vector to store the draft tokens, initially empty.
+void common_ngram_map_draft(
+    common_ngram_map & map,
+    const llama_tokens & inp, llama_token sampled,
+    llama_tokens & draft);
+
+// Update the statistics of a value after a draft was processed.
+void common_ngram_map_accept(common_ngram_map & map, uint16_t n_accepted);

common/ngram-mod.cpp

@@ -0,0 +1,60 @@
+#include "ngram-mod.h"
+
+//
+// common_ngram_mod
+//
+
+common_ngram_mod::common_ngram_mod(uint16_t n, size_t size) : n(n), used(0) {
+    entries.resize(size);
+
+    reset();
+}
+
+size_t common_ngram_mod::idx(const entry_t * tokens) const {
+    size_t res = 0;
+
+    for (size_t i = 0; i < n; ++i) {
+        res = res*6364136223846793005ULL + tokens[i];
+    }
+
+    res = res % entries.size();
+
+    return res;
+}
+
+void common_ngram_mod::add(const entry_t * tokens) {
+    const size_t i = idx(tokens);
+
+    if (entries[i] == EMPTY) {
+        used++;
+    }
+
+    entries[i] = tokens[n];
+}
+
+common_ngram_mod::entry_t common_ngram_mod::get(const entry_t * tokens) const {
+    const size_t i = idx(tokens);
+
+    return entries[i];
+}
+
+void common_ngram_mod::reset() {
+    std::fill(entries.begin(), entries.end(), EMPTY);
+    used = 0;
+}
+
+size_t common_ngram_mod::get_n() const {
+    return n;
+}
+
+size_t common_ngram_mod::get_used() const {
+    return used;
+}
+
+size_t common_ngram_mod::size() const {
+    return entries.size();
+}
+
+size_t common_ngram_mod::size_bytes() const {
+    return entries.size() * sizeof(entries[0]);
+}

common/ngram-mod.h

@@ -0,0 +1,38 @@
+#pragma once
+
+#include <cstdint>
+#include <vector>
+#include <cstddef>
+
+//
+// common_ngram_mod
+// ref: https://github.com/ggml-org/llama.cpp/pull/19164
+//
+
+// basic n-gram hasher
+struct common_ngram_mod {
+    using entry_t = int32_t;
+
+    static constexpr entry_t EMPTY = -1;
+
+    common_ngram_mod(uint16_t n, size_t size);
+
+    size_t  idx(const entry_t * tokens) const;
+    void    add(const entry_t * tokens);
+    entry_t get(const entry_t * tokens) const; // return -1 if not found
+
+    void reset();
+
+    size_t get_n()    const;
+    size_t get_used() const;
+
+    size_t size()       const;
+    size_t size_bytes() const;
+
+private:
+    size_t n; // ngram size to hash
+
+    size_t used;
+
+    std::vector<entry_t> entries;
+};

common/speculative.h

@@ -5,31 +5,37 @@
 
 struct common_speculative;
 
-struct common_speculative_params {
-    int n_draft = 16;  // max drafted tokens
-    int n_reuse = 256;
+// comma separated list of all types
+std::string common_speculative_type_name_str();
 
-    float p_min = 0.75f; // min probability required to accept a token in the draft
-};
+// convert string to type
+enum common_speculative_type common_speculative_type_from_name(const std::string & name);
 
-struct common_speculative * common_speculative_init(
-        struct llama_context * ctx_tgt,
-        struct llama_context * ctx_dft
-);
+// convert type to string
+std::string common_speculative_type_to_str(enum common_speculative_type type);
 
-void common_speculative_free(struct common_speculative * spec);
+// check if the llama_context is compatible for speculative decoding
+// note: clears the memory of the context
+bool common_speculative_is_compat(llama_context * ctx_tgt);
 
-bool common_speculative_are_compatible(
-        const struct llama_context * ctx_tgt,
-        const struct llama_context * ctx_dft);
+common_speculative * common_speculative_init(
+        common_params_speculative & params,
+        llama_context             * ctx_tgt);
 
-void common_speculative_add_replacement_tgt_dft(
-        struct common_speculative * spec,
-        const char *source, const char *dest);
+void common_speculative_free(common_speculative * spec);
+
+// optionally call once at the beginning of a new generation
+void common_speculative_begin(common_speculative * spec, const llama_tokens & prompt);
 
 // sample up to n_draft tokens and add them to the batch using the draft model
-llama_tokens common_speculative_gen_draft(
-               struct common_speculative * spec,
-        struct common_speculative_params   params,
-                      const llama_tokens & prompt,
-                             llama_token   id_last);
+llama_tokens common_speculative_draft(
+                     common_speculative * spec,
+        const common_params_speculative & params,
+                     const llama_tokens & prompt,
+                            llama_token   id_last);
+
+// informs the speculative decoder that n_accepted tokens were accepted by the target model
+void common_speculative_accept(common_speculative * spec, uint16_t n_accepted);
+
+// print statistics about the speculative decoding
+void common_speculative_print_stats(const common_speculative * spec);

convert_hf_to_gguf.py

@@ -586,6 +586,10 @@ class ModelBase:
                             gguf.MODEL_TENSOR.A_ENC_EMBD_POS,
                             gguf.MODEL_TENSOR.ALTUP_CORRECT_COEF,
                             gguf.MODEL_TENSOR.ALTUP_PREDICT_COEF,
+                            # Kimi KDA conv weights should be F32
+                            gguf.MODEL_TENSOR.SSM_CONV1D_Q,
+                            gguf.MODEL_TENSOR.SSM_CONV1D_K,
+                            gguf.MODEL_TENSOR.SSM_CONV1D_V,
                         )
                     )
                     or new_name[-7:] not in (".weight", ".lora_a", ".lora_b")
@@ -903,10 +907,10 @@ class TextModel(ModelBase):
         if (f_norm_eps := self.find_hparam(["layer_norm_eps", "layer_norm_epsilon", "norm_epsilon"], optional=True)) is not None:
             self.gguf_writer.add_layer_norm_eps(f_norm_eps)
             logger.info(f"gguf: layer norm epsilon = {f_norm_eps}")
-        if (n_experts := self.hparams.get("num_local_experts")) is not None:
+        if (n_experts := self.find_hparam(["num_local_experts", "num_experts"], optional=True)) is not None:
             self.gguf_writer.add_expert_count(n_experts)
             logger.info(f"gguf: expert count = {n_experts}")
-        if (n_experts_used := self.hparams.get("num_experts_per_tok")) is not None:
+        if (n_experts_used := self.find_hparam(["num_experts_per_tok", "num_experts_per_token"], optional=True)) is not None:
             self.gguf_writer.add_expert_used_count(n_experts_used)
             logger.info(f"gguf: experts used count = {n_experts_used}")
         if (n_expert_groups := self.hparams.get("n_group")) is not None:
@@ -916,7 +920,7 @@ class TextModel(ModelBase):
             self.gguf_writer.add_expert_group_used_count(n_group_used)
             logger.info(f"gguf: expert groups used count = {n_group_used}")
 
-        if (score_func := self.find_hparam(["score_function", "scoring_func", "score_func"], optional=True)) is not None:
+        if (score_func := self.find_hparam(["score_function", "scoring_func", "score_func", "moe_router_activation", "moe_router_activation_func"], optional=True)) is not None:
             if score_func == "sigmoid":
                 self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID)
             elif score_func == "softmax":
@@ -1257,6 +1261,9 @@ class TextModel(ModelBase):
         if chkhsh == "6c81ce329e0802883b22eabab0d3fa48357337ef1ecb45443828bf1f6254833f":
             # ref: https://huggingface.co/LGAI-EXAONE/K-EXAONE-236B-A23B
             res = "exaone-moe"
+        if chkhsh == "d30d75d9059f1aa2c19359de71047b3ae408c70875e8a3ccf8c5fba56c9d8af4":
+            # ref: https://huggingface.co/Qwen/Qwen3.5-9B-Instruct
+            res = "qwen35"
 
         if res is None:
             logger.warning("\n")
@@ -4105,37 +4112,29 @@ class Qwen2MoeModel(TextModel):
         # Expected GGML ne: {n_embd, n_ff_exp, n_expert} for gate/up, {n_ff_exp, n_embd, n_expert} for down
         if name.endswith("mlp.experts.down_proj") or name.endswith("mlp.experts.down_proj.weight"):
             mapped = f"{name}.weight" if not name.endswith(".weight") else name
-            # Input: (n_expert=128, n_ff_exp=768, n_embd=2048)
-            # Want GGML ne: {n_ff_exp, n_embd, n_expert} = {768, 2048, 128}
-            # Need PyTorch: (128, 2048, 768) [reversed of GGML]
-            # So: permute(0, 2, 1): (128, 768, 2048) -> (128, 2048, 768)
-            permuted = data_torch.permute(0, 2, 1).contiguous()
-            yield from super().modify_tensors(permuted, mapped, bid)
+            # HF: [n_expert, n_embd, n_ff] -> GGML: {n_ff, n_embd, n_expert}
+            yield from super().modify_tensors(data_torch, mapped, bid)
             return
 
         if name.endswith("mlp.experts.gate_up_proj") or name.endswith("mlp.experts.gate_up_proj.weight"):
-            if data_torch.ndim < 3 or data_torch.shape[-1] % 2 != 0:
+            if data_torch.ndim < 3 or data_torch.shape[-2] % 2 != 0:
                 raise ValueError(f"Unexpected gate_up_proj shape for {name}: {tuple(data_torch.shape)}")
-            split_dim = data_torch.shape[-1] // 2
-            gate = data_torch[..., :split_dim].contiguous()
-            up = data_torch[..., split_dim:].contiguous()
-            # Input gate/up: (n_expert=128, n_embd=2048, n_ff_exp=768)
-            # Want GGML ne: {n_embd, n_ff_exp, n_expert} = {2048, 768, 128}
-            # Need PyTorch: (128, 768, 2048) [reversed of GGML]
-            # So: permute(0, 2, 1): (128, 2048, 768) -> (128, 768, 2048)
-            base_name = name.removesuffix(".weight")
-            base = base_name.rsplit('.', 1)[0]
-            mapped_gate = f"{base}.gate_proj.weight"
-            mapped_up = f"{base}.up_proj.weight"
-            perm_gate = gate.permute(0, 2, 1).contiguous()
-            perm_up = up.permute(0, 2, 1).contiguous()
-            yield from super().modify_tensors(perm_gate, mapped_gate, bid)
-            yield from super().modify_tensors(perm_up, mapped_up, bid)
+            # HF: [n_expert, 2*n_ff, n_embd] -> split on dim=-2
+            n_ff = data_torch.shape[-2] // 2
+            gate = data_torch[..., :n_ff, :].contiguous()
+            up = data_torch[..., n_ff:, :].contiguous()
+            # gate/up: [n_expert, n_ff, n_embd] -> GGML: {n_embd, n_ff, n_expert}
+            base_name = name.removesuffix(".weight").removesuffix(".gate_up_proj")
+            mapped_gate = f"{base_name}.gate_proj.weight"
+            mapped_up = f"{base_name}.up_proj.weight"
+            yield from super().modify_tensors(gate, mapped_gate, bid)
+            yield from super().modify_tensors(up, mapped_up, bid)
             return
 
         if name.startswith("mlp") or name.startswith("vision_model") or name.startswith("model.vision_tower") or name.startswith("model.multi_modal_projector") or name.startswith("model.visual"):
             # skip visual tensors
             return
+
         if name.find("experts") != -1:
             n_experts = self.hparams["num_experts"]
             assert bid is not None
@@ -4291,6 +4290,7 @@ class Qwen3NextModel(Qwen2MoeModel):
         self.gguf_writer.add_ssm_group_count(self.hparams["linear_num_key_heads"])
         self.gguf_writer.add_ssm_time_step_rank(self.hparams["linear_num_value_heads"])
         self.gguf_writer.add_ssm_inner_size(self.hparams["linear_value_head_dim"] * self.hparams["linear_num_value_heads"])
+        self.gguf_writer.add_full_attention_interval(self.hparams.get("full_attention_interval", 4))
         if (rope_dim := self.hparams.get("head_dim")) is None:
             rope_dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
         self.gguf_writer.add_rope_dimension_count(int(rope_dim * self.hparams.get("partial_rotary_factor", 0.25)))
@@ -4355,7 +4355,7 @@ class RND1Model(Qwen2MoeModel):
             self.gguf_writer.add_mask_token_id(mask_token_id)
 
 
-@ModelBase.register("Qwen3VLForConditionalGeneration", "Qwen3VLMoeForConditionalGeneration")
+@ModelBase.register("Qwen3VLForConditionalGeneration", "Qwen3VLMoeForConditionalGeneration", "Qwen3_5ForConditionalGeneration", "Qwen3_5MoeForConditionalGeneration")
 class Qwen3VLVisionModel(MmprojModel):
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
@@ -4401,6 +4401,10 @@ class Qwen3VLVisionModel(MmprojModel):
         if name.startswith("model.language_model.") or name.startswith("lm_head."):
             return
 
+        # Skip MTP tensors
+        if name.startswith("mtp."):
+            return
+
         if name.startswith("model.visual."):
             name = name.replace("model.visual.", "visual.", 1)
 
@@ -4531,9 +4535,125 @@ class Qwen3VLMoeTextModel(Qwen3MoeModel):
         if name.startswith("model.visual."):
             return
 
+        # Qwen3VL has transposed packed tensors, so we treat it differently from general Qwen2MoE packed tensors
+        if name.endswith("mlp.experts.down_proj") or name.endswith("mlp.experts.down_proj.weight"):
+            name = name.replace("language_model.", "")
+            mapped = f"{name}.weight" if not name.endswith(".weight") else name
+            permuted = data_torch.permute(0, 2, 1).contiguous()
+            yield from ModelBase.modify_tensors(self, permuted, mapped, bid)
+            return
+
+        if name.endswith("mlp.experts.gate_up_proj") or name.endswith("mlp.experts.gate_up_proj.weight"):
+            name = name.replace("language_model.", "")
+            if data_torch.ndim < 3 or data_torch.shape[-1] % 2 != 0:
+                raise ValueError(f"Unexpected gate_up_proj shape for {name}: {tuple(data_torch.shape)}")
+            split_dim = data_torch.shape[-1] // 2
+            gate = data_torch[..., :split_dim].contiguous()
+            up = data_torch[..., split_dim:].contiguous()
+            # Input gate/up: (n_expert=128, n_embd=2048, n_ff_exp=768)
+            # Want GGML ne: {n_embd, n_ff_exp, n_expert} = {2048, 768, 128}
+            # Need PyTorch: (128, 768, 2048) [reversed of GGML]
+            # So: permute(0, 2, 1): (128, 2048, 768) -> (128, 768, 2048)
+            base_name = name.removesuffix(".weight")
+            base = base_name.rsplit('.', 1)[0]
+            mapped_gate = f"{base}.gate_proj.weight"
+            mapped_up = f"{base}.up_proj.weight"
+            perm_gate = gate.permute(0, 2, 1).contiguous()
+            perm_up = up.permute(0, 2, 1).contiguous()
+            yield from ModelBase.modify_tensors(self, perm_gate, mapped_gate, bid)
+            yield from ModelBase.modify_tensors(self, perm_up, mapped_up, bid)
+            return
+
         yield from super().modify_tensors(data_torch, name, bid)
 
 
+class _LinearAttentionVReorderBase(Qwen3NextModel):
+    model_arch = gguf.MODEL_ARCH.QWEN3NEXT  # overridden by subclasses
+    """reorders V heads from grouped to tiled order for ggml broadcast
+
+    see https://github.com/ggml-org/llama.cpp/pull/19468#discussion_r2786394306
+
+    Linear attention may has num_k_heads < num_v_heads. The HF weights store
+    V heads grouped by K head: [G0_v0..v{r-1}, G1_v0..v{r-1}, ...].
+    ggml binary ops use tiled broadcast: [K0, K1, ..., K0, K1, ...].
+    We reorder V heads to tiled order so ggml_repeat can replace the expensive
+    interleaved repeat: [G0_v0, G1_v0, ..., G0_v1, G1_v1, ...].
+    """
+
+    @staticmethod
+    def _reorder_v_heads(tensor: Tensor, dim: int, num_k_heads: int, num_v_per_k: int, head_dim: int) -> Tensor:
+        """Reorder V heads from grouped (by K head) to tiled order along the given dimension."""
+        shape = list(tensor.shape)
+        if dim < 0:
+            dim += len(shape)
+        new_shape = shape[:dim] + [num_k_heads, num_v_per_k, head_dim] + shape[dim + 1:]
+        tensor = tensor.reshape(*new_shape)
+        perm = list(range(len(new_shape)))
+        perm[dim], perm[dim + 1] = perm[dim + 1], perm[dim]
+        return tensor.permute(*perm).contiguous().reshape(*shape)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        num_k_heads = self.hparams.get("linear_num_key_heads", 0)
+        num_v_heads = self.hparams.get("linear_num_value_heads", 0)
+
+        if num_k_heads > 0 and num_v_heads > 0 and num_k_heads != num_v_heads and "linear_attn." in name:
+            head_k_dim = self.hparams["linear_key_head_dim"]
+            head_v_dim = self.hparams["linear_value_head_dim"]
+            num_v_per_k = num_v_heads // num_k_heads
+
+            if ".in_proj_qkv." in name:
+                # QKV weight: reorder only the V rows
+                q_dim = head_k_dim * num_k_heads
+                k_dim = head_k_dim * num_k_heads
+                q = data_torch[:q_dim]
+                k = data_torch[q_dim:q_dim + k_dim]
+                v = data_torch[q_dim + k_dim:]
+                v = self._reorder_v_heads(v, 0, num_k_heads, num_v_per_k, head_v_dim)
+                data_torch = torch.cat([q, k, v], dim=0)
+
+            elif ".in_proj_z." in name:
+                # Z gate weight: reorder rows (num_v_heads * head_v_dim)
+                data_torch = self._reorder_v_heads(data_torch, 0, num_k_heads, num_v_per_k, head_v_dim)
+
+            elif ".in_proj_b." in name or ".in_proj_a." in name:
+                # Beta/Alpha weight: reorder rows (num_v_heads, head_dim=1)
+                data_torch = self._reorder_v_heads(data_torch, 0, num_k_heads, num_v_per_k, 1)
+
+            elif ".A_log" in name or ".dt_bias" in name or ".dt_proj" in name:
+                # A_log / dt_bias: 1D parameters with num_v_heads elements
+                if data_torch.ndim == 1:
+                    data_torch = self._reorder_v_heads(
+                        data_torch.unsqueeze(-1), 0, num_k_heads, num_v_per_k, 1
+                    ).squeeze(-1)
+                else:
+                    data_torch = self._reorder_v_heads(data_torch, -1, num_k_heads, num_v_per_k, 1)
+
+            elif ".conv1d" in name:
+                # Conv1d kernel: reorder only the V channel portion
+                data = data_torch.squeeze()
+                qk_channels = head_k_dim * num_k_heads * 2
+                qk_part = data[:qk_channels]
+                v_part = data[qk_channels:]
+                v_part = self._reorder_v_heads(v_part, 0, num_k_heads, num_v_per_k, head_v_dim)
+                data_torch = torch.cat([qk_part, v_part], dim=0)
+
+            elif ".out_proj." in name:
+                # Out projection weight: reorder columns (input dimension)
+                data_torch = self._reorder_v_heads(data_torch, 1, num_k_heads, num_v_per_k, head_v_dim)
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+
+@ModelBase.register("Qwen3_5ForConditionalGeneration")
+class Qwen3_5TextModel(_LinearAttentionVReorderBase):
+    model_arch = gguf.MODEL_ARCH.QWEN35
+
+
+@ModelBase.register("Qwen3_5MoeForConditionalGeneration")
+class Qwen3_5MoeTextModel(_LinearAttentionVReorderBase):
+    model_arch = gguf.MODEL_ARCH.QWEN35MOE
+
+
 @ModelBase.register("GPT2LMHeadModel")
 class GPT2Model(TextModel):
     model_arch = gguf.MODEL_ARCH.GPT2
@@ -5013,6 +5133,221 @@ class CodeShellModel(TextModel):
         self.gguf_writer.add_rope_scaling_factor(1.0)
 
 
+@ModelBase.register("KimiLinearModel", "KimiLinearForCausalLM")
+class KimiLinearModel(TextModel):
+    """Kimi-Linear model with hybrid MLA+KDA architecture"""
+    model_arch = gguf.MODEL_ARCH.KIMI_LINEAR
+
+    _experts: list[dict[str, Tensor]] | None = None
+
+    def set_vocab(self):
+        try:
+            self._set_vocab_gpt2()
+            return
+        except Exception:
+            pass
+
+        from transformers import AutoTokenizer
+        tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True)
+        tokpre = self.get_vocab_base_pre(tokenizer)
+
+        if tokpre == "kimi-k2":
+            # Build merges list using the approach similar to HunYuanMoE
+            merges = []
+            vocab = {}
+            mergeable_ranks = tokenizer.model._mergeable_ranks
+            for token, rank in mergeable_ranks.items():
+                vocab[QwenModel.token_bytes_to_string(token)] = rank
+                if len(token) == 1:
+                    continue
+                merged = QwenModel.bpe(mergeable_ranks, token, max_rank=rank)
+                if len(merged) == 2:
+                    merges.append(' '.join(map(QwenModel.token_bytes_to_string, merged)))
+            # Build token list
+            vocab_size = self.hparams["vocab_size"]
+            special_tokens = tokenizer.special_tokens
+            reverse_vocab = {id_ : encoded_tok for encoded_tok, id_ in {**vocab, **special_tokens}.items()}
+            tokens: list[str] = []
+            toktypes: list[int] = []
+
+            for i in range(vocab_size):
+                if i not in reverse_vocab:
+                    tokens.append(f"[PAD{i}]")
+                    toktypes.append(gguf.TokenType.UNUSED)
+                else:
+                    token = reverse_vocab[i]
+                    tokens.append(token)
+                    if i in special_tokens.values():
+                        toktypes.append(gguf.TokenType.CONTROL)
+                    else:
+                        toktypes.append(gguf.TokenType.NORMAL)
+
+            self.gguf_writer.add_tokenizer_model("gpt2")
+            self.gguf_writer.add_tokenizer_pre(tokpre)
+            self.gguf_writer.add_token_list(tokens)
+            self.gguf_writer.add_token_types(toktypes)
+            self.gguf_writer.add_token_merges(merges)
+
+            special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False)
+            special_vocab.add_to_gguf(self.gguf_writer)
+            # override eos id in config.json with tiktoken eos id
+            self.gguf_writer.add_eos_token_id(tokenizer.eos_id)
+        else:
+            raise NotImplementedError(f"Deepseek pre-tokenizer {tokpre!r} is not supported yet!")
+
+    def set_gguf_parameters(self):
+        # note: To enable MLA KV cache, attention needs to be converted into MQA (ie: GQA with 1 group)
+        self.hparams["num_key_value_heads"] = 1
+
+        super().set_gguf_parameters()
+        self.gguf_writer.add_vocab_size(self.hparams["vocab_size"])
+
+        # KDA & MLA params
+        # Get ssm_d_conv from linear_attn_config.short_conv_kernel_size or ssm_d_conv
+        linear_attn_config = self.hparams["linear_attn_config"]
+        # n_head == 0 for KDA layers, n_head > 0 for MLA layers
+        # full_attention_layers list will be used to distingush layer type
+        _num_kv_heads = list()
+        _full_attn_layers = linear_attn_config["full_attn_layers"]
+        for il in range(self.hparams["num_hidden_layers"]):
+            if il + 1 in _full_attn_layers:
+                _num_kv_heads.append(self.hparams["num_key_value_heads"])
+            else:
+                _num_kv_heads.append(0)
+        assert len(_num_kv_heads) == self.hparams["num_hidden_layers"]
+        self.gguf_writer.add_head_count_kv(_num_kv_heads)
+
+        if (ssm_d_conv := linear_attn_config.get("short_conv_kernel_size")) is not None:
+            self.gguf_writer.add_ssm_conv_kernel(ssm_d_conv)
+        if (kda_head_dim := linear_attn_config.get("head_dim")) is not None:
+            self.gguf_writer.add_kda_head_dim(kda_head_dim)
+
+        # MLA params - use add_* methods that handle arch substitution
+        # Support both HuggingFace naming (q_lora_rank, kv_lora_rank) and internal naming (n_lora_q, n_lora_kv)
+        if (q_lora_rank := self.find_hparam(["q_lora_rank", "n_lora_q"], optional=True)) is not None:
+            self.gguf_writer.add_q_lora_rank(q_lora_rank)
+        # To enable MLA KV cache, MLA needs to be converted into MQA with larger heads, then decompresses to MHA
+        kv_lora_rank = self.find_hparam(["kv_lora_rank", "n_lora_kv"], optional=False)
+        self.gguf_writer.add_kv_lora_rank(kv_lora_rank)
+
+        # MLA head dimensions
+        # Support HuggingFace naming: qk_nope_head_dim, qk_rope_head_dim, v_head_dim
+        qk_nope_head_dim = self.hparams.get("qk_nope_head_dim")
+        # Rotation - use qk_rope_head_dim for Kimi
+        qk_rope_head_dim = self.find_hparam(["qk_rope_head_dim", "n_rot"], optional=False)
+        self.gguf_writer.add_rope_dimension_count(qk_rope_head_dim)
+        self.gguf_writer.add_key_length(kv_lora_rank + qk_rope_head_dim)
+        v_head_dim = self.hparams.get("v_head_dim")
+
+        # Calculate n_embd_head_k_mla = qk_nope_head_dim + qk_rope_head_dim
+        if (n_embd_head_k_mla := self.find_hparam(["n_embd_head_k_mla"], optional=True)) is not None:
+            self.gguf_writer.add_key_length_mla(n_embd_head_k_mla)
+        elif qk_nope_head_dim is not None:
+            n_embd_head_k_mla = qk_nope_head_dim + qk_rope_head_dim
+            self.gguf_writer.add_key_length_mla(n_embd_head_k_mla)
+
+        # n_embd_head_v_mla = v_head_dim
+        if (n_embd_head_v_mla := self.hparams.get("n_embd_head_v_mla")) is not None:
+            self.gguf_writer.add_value_length_mla(n_embd_head_v_mla)
+        elif v_head_dim is not None:
+            self.gguf_writer.add_value_length_mla(v_head_dim)
+
+        # moe_intermediate_size (1024 for Kimi)
+        self.gguf_writer.add_expert_feed_forward_length(self.hparams["moe_intermediate_size"])
+        # num_shared_experts (1 for Kimi)
+        self.gguf_writer.add_expert_shared_count(self.hparams["num_shared_experts"])
+        # first_k_dense_replace (1 for Kimi - first layer uses dense MLP)
+        self.gguf_writer.add_leading_dense_block_count(self.hparams["first_k_dense_replace"])
+        # Routed scaling factor (expert_weights_scale = 2.446 for Kimi)
+        self.gguf_writer.add_expert_weights_scale(self.hparams["routed_scaling_factor"])
+
+    def prepare_tensors(self):
+        super().prepare_tensors()
+        if self._experts is not None:
+            experts = [k for d in self._experts for k in d.keys()]
+            if len(experts) > 0:
+                raise ValueError(f"Unprocessed experts: {experts}")
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        logger.info(f"Processing {name}: shape before = {tuple(data_torch.shape)}")
+
+        # Handle KDA conv1d weights
+        # HuggingFace/vLLM stores as [d_inner, d_conv] (2D), memory layout: conv_step changes fastest
+        # llama.cpp expects ggml ne = [d_conv, 1, d_inner, 1], memory layout: ne[0]=d_conv changes fastest
+        # GGUF reverses numpy shape when writing, so numpy (1, d_inner, 1, d_conv) -> ggml ne = [d_conv, 1, d_inner, 1]
+        # Memory layouts match: both have conv_step (d_conv) changing fastest
+        if name.endswith((".q_conv1d.weight", ".k_conv1d.weight", ".v_conv1d.weight")):
+            # HF shape: [d_inner, d_conv] e.g. [4096, 4]
+            # Target numpy shape: (1, d_inner, 1, d_conv) -> ggml ne = [d_conv, 1, d_inner, 1]
+            if data_torch.ndim == 2:
+                d_inner, d_conv = data_torch.shape
+                # Reshape to (1, d_inner, 1, d_conv) - memory layout preserved (d_conv fastest)
+                data_torch = data_torch.reshape(1, d_inner, 1, d_conv)
+                logger.info(f"Reshaped conv1d weight {name}: [d_inner={d_inner}, d_conv={d_conv}] -> numpy {tuple(data_torch.shape)} -> ggml ne=[{d_conv}, 1, {d_inner}, 1]")
+            elif data_torch.ndim == 3:
+                # Already 3D [d_inner, 1, d_conv] from unsqueeze
+                d_inner, _, d_conv = data_torch.shape
+                data_torch = data_torch.reshape(1, d_inner, 1, d_conv)
+                logger.info(f"Reshaped conv1d weight {name}: [d_inner={d_inner}, 1, d_conv={d_conv}] -> numpy {tuple(data_torch.shape)} -> ggml ne=[{d_conv}, 1, {d_inner}, 1]")
+
+        # Kimi specific bias
+        if name.endswith("e_score_correction_bias"):
+            name = name.replace("e_score_correction_bias", "e_score_correction.bias")
+
+        # Handle A_log: iHF stores as [1, 1, num_heads, 1]
+        # llama.cpp expects ggml ne = [1, num_heads, 1, 1]
+        # GGUF reverses numpy shape: numpy (1, 1, num_heads, 1) -> ggml ne = [1, num_heads, 1, 1]
+        if name.endswith(".A_log"):
+            data_torch = -torch.exp(data_torch)
+        if name.endswith(".dt_bias"):
+            name = name.rpartition(".dt_bias")[0] + ".dt_proj.bias"
+            logger.info("Changed dt_bias to dt_proj.bias")
+
+        # process the experts separately
+        if name.find("block_sparse_moe.experts") != -1:
+            n_experts = self.find_hparam(["num_local_experts", "num_experts"], optional=False)
+            assert bid is not None
+
+            if self._experts is None:
+                self._experts = [{} for _ in range(self.block_count)]
+
+            self._experts[bid][name] = data_torch
+
+            if len(self._experts[bid]) >= n_experts * 3:
+                # merge the experts into a single 3d tensor
+                # w1: gate, w2: down, w3: up
+                for wid, tname in [("w1", gguf.MODEL_TENSOR.FFN_GATE_EXP),
+                                   ("w2", gguf.MODEL_TENSOR.FFN_DOWN_EXP),
+                                   ("w3", gguf.MODEL_TENSOR.FFN_UP_EXP)]:
+                    datas: list[Tensor] = []
+                    for xid in range(n_experts):
+                        ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.{wid}.weight"
+                        datas.append(self._experts[bid][ename])
+                        del self._experts[bid][ename]
+                    data_torch = torch.stack(datas, dim=0)
+                    new_name = self.format_tensor_name(tname, bid)
+                    yield from super().modify_tensors(data_torch, new_name, bid)
+            return
+
+        # note: MLA with the absorption optimization, needs these two split and k_b_proj transposed
+        if name.endswith("kv_b_proj.weight"):
+            name_kb = name.replace("kv_b_proj", "k_b_proj")
+            name_vb = name.replace("kv_b_proj", "v_b_proj")
+            n_head_kv = self.hparams["num_key_value_heads"]
+            v_head_dim = self.find_hparam(["n_embd_head_v_mla", "v_head_dim"], optional=False)
+            qk_nope_head_dim = self.hparams["qk_nope_head_dim"]
+            logger.info("Split kv_b n_head_kv %d\n" % n_head_kv)
+            assert data_torch.shape[0] == n_head_kv * (v_head_dim + qk_nope_head_dim)
+            kv_b = data_torch.view(n_head_kv, v_head_dim + qk_nope_head_dim, data_torch.shape[-1])
+            k_b, v_b = torch.split(kv_b, [qk_nope_head_dim, v_head_dim], dim=1)
+            k_b = k_b.transpose(1, 2)
+            yield from super().modify_tensors(k_b, name_kb, bid)
+            yield from super().modify_tensors(v_b, name_vb, bid)
+            return
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+
 @ModelBase.register("InternLM2ForCausalLM")
 class InternLM2Model(TextModel):
     model_arch = gguf.MODEL_ARCH.INTERNLM2
@@ -7693,6 +8028,135 @@ class MimoV2Model(TextModel):
                 raise ValueError(f"Unprocessed experts: {experts}")
 
 
+@ModelBase.register("Step3p5ForCausalLM")
+class Step35Model(TextModel):
+    model_arch = gguf.MODEL_ARCH.STEP35
+
+    def set_gguf_parameters(self):
+        rope_theta = self.hparams.get("rope_theta")
+        if isinstance(rope_theta, list):
+            self.hparams["rope_theta"] = float(rope_theta[0])
+            self.hparams["local_rope_theta"] = float(rope_theta[1])
+            self.rope_parameters["rope_theta"] = self.hparams["rope_theta"]
+            self.rope_parameters["sliding_attention"] = {"rope_theta": self.hparams["local_rope_theta"]}
+
+        super().set_gguf_parameters()
+
+        layer_types = self.hparams.get("layer_types") or []
+        partial_rotary_factors = self.hparams.get("partial_rotary_factors") or []
+        attn_other = self.hparams.get("attention_other_setting") or {}
+
+        n_head_base = self.hparams["num_attention_heads"]
+        n_kv_base = self.hparams["num_attention_groups"]
+
+        n_head_swa = attn_other.get("num_attention_heads", n_head_base)
+        n_kv_swa = attn_other.get("num_attention_groups", n_kv_base)
+
+        layer_types = layer_types[: self.block_count]
+        partial_rotary_factors = partial_rotary_factors[: self.block_count]
+        assert [1.0 if lt == "sliding_attention" else 0.5 for lt in layer_types] == partial_rotary_factors
+        head_arr = [n_head_swa if lt == "sliding_attention" else n_head_base for lt in layer_types]
+        kv_arr = [n_kv_swa if lt == "sliding_attention" else n_kv_base for lt in layer_types]
+        swa_pat = [lt == "sliding_attention" for lt in layer_types]
+
+        self.gguf_writer.add_head_count(head_arr)
+        self.gguf_writer.add_head_count_kv(kv_arr)
+
+        self.gguf_writer.add_sliding_window(self.hparams["sliding_window"])
+        self.gguf_writer.add_sliding_window_pattern(swa_pat)
+
+        self.gguf_writer.add_value_length(self.hparams["head_dim"])
+
+        # MoE params
+        self.gguf_writer.add_expert_count(self.hparams["moe_num_experts"])
+        self.gguf_writer.add_expert_used_count(self.hparams["moe_top_k"])
+        self.gguf_writer.add_expert_feed_forward_length(self.hparams["moe_intermediate_size"])
+        self.gguf_writer.add_expert_shared_feed_forward_length(self.hparams["share_expert_dim"])
+
+        if (moe_router_scaling_factor := self.hparams.get("moe_router_scaling_factor")) is not None:
+            self.gguf_writer.add_expert_weights_scale(moe_router_scaling_factor)
+        if (norm_expert_weight := self.hparams.get("norm_expert_weight")) is not None:
+            self.gguf_writer.add_expert_weights_norm(norm_expert_weight)
+
+        # leading dense blocks
+        leading_dense = 0
+        moe_layers_enum = self.hparams.get("moe_layers_enum")
+        if isinstance(moe_layers_enum, str) and moe_layers_enum.strip():
+            moe_layers = sorted(int(i) for i in moe_layers_enum.strip().split(","))
+            if moe_layers:
+                leading_dense = max(0, moe_layers[0])
+        self.gguf_writer.add_leading_dense_block_count(leading_dense)
+        self.gguf_writer.add_moe_every_n_layers(int(self.hparams.get("moe_every_n_layer", 1)))
+
+        self.gguf_writer.add_layer_norm_rms_eps(self.hparams.get("rms_norm_eps", 1e-5))
+
+        # Optional per-layer SwiGLU clamps.
+        if (limits := self.hparams.get("swiglu_limits")) is not None:
+            limits_f = [0.0 if v is None else float(v) for v in limits[: self.block_count]]
+            self.gguf_writer.add_swiglu_clamp_exp(limits_f)
+        if (limits_shared := self.hparams.get("swiglu_limits_shared")) is not None:
+            limits_shared_f = [0.0 if v is None else float(v) for v in limits_shared[: self.block_count]]
+            self.gguf_writer.add_swiglu_clamp_shexp(limits_shared_f)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None):
+        # remove mtp layers
+        if (m := re.match(r"model\.layers\.(\d+)\.", name)) is not None:
+            il = int(m.group(1))
+            n_main = int(self.hparams.get("num_hidden_layers", self.block_count))
+            if il >= n_main:
+                return
+        if name.endswith("norm.weight"):
+            data_torch += 1.0
+        # Map router bias (expert selection bias) to a GGUF bias tensor
+        if name.endswith(".moe.router_bias"):
+            name += ".bias"
+
+        if name.endswith((".self_attn.g_proj.weight", ".moe.gate.weight", ".moe.up_proj.weight", ".moe.gate_proj.weight", ".moe.down_proj.weight")):
+            data_torch = data_torch.squeeze().contiguous()
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
+        # Step35 can optionally use Llama-3 style RoPE scaling (HF: rope_scaling.rope_type == "llama3").
+        # llama.cpp represents this via a single extra tensor: "rope_freqs.weight" (aka MODEL_TENSOR.ROPE_FREQS).
+        rope_params = self.rope_parameters.get("full_attention", self.rope_parameters)
+        rope_type = rope_params.get("rope_type") or ""
+        if rope_type.lower() != "llama3":
+            return
+
+        # Step35 configs can carry per-layer rope_theta as a list; for llama3 rope factors we use the base value.
+        rope_theta = self.hparams.get("rope_theta", 10000.0)
+        if isinstance(rope_theta, list):
+            rope_theta = rope_theta[0]
+        base = float(rope_theta)
+        if (dim := self.hparams.get("head_dim")) is None:
+            dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
+        dim = int(dim)
+
+        freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
+
+        factor = float(rope_params.get("factor", 8.0))
+        low_freq_factor = float(rope_params.get("low_freq_factor", 1.0))
+        high_freq_factor = float(rope_params.get("high_freq_factor", 4.0))
+        old_context_len = int(rope_params.get("original_max_position_embeddings", self.hparams.get("original_max_position_embeddings", 8192)))
+
+        low_freq_wavelen = old_context_len / low_freq_factor
+        high_freq_wavelen = old_context_len / high_freq_factor
+
+        rope_factors: list[float] = []
+        for freq in freqs:
+            wavelen = 2 * math.pi / float(freq)
+            if wavelen < high_freq_wavelen:
+                rope_factors.append(1.0)
+            elif wavelen > low_freq_wavelen:
+                rope_factors.append(factor)
+            else:
+                smooth = (old_context_len / wavelen - low_freq_factor) / (high_freq_factor - low_freq_factor)
+                rope_factors.append(1.0 / ((1.0 - smooth) / factor + smooth))
+
+        yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), torch.tensor(rope_factors, dtype=torch.float32))
+
+
 @ModelBase.register("PanguEmbeddedForCausalLM")
 class PanguEmbeddedModel(TextModel):
     model_arch = gguf.MODEL_ARCH.PANGU_EMBED
@@ -8806,6 +9270,7 @@ class GraniteMoeModel(GraniteModel):
             gate, up = data_torch.split(ffn_dim, dim=-2)
             yield from ModelBase.modify_tensors(self, gate, self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE_EXP, bid), bid)
             yield from ModelBase.modify_tensors(self, up, self.format_tensor_name(gguf.MODEL_TENSOR.FFN_UP_EXP, bid), bid)
+            return
 
         has_experts = bool(self.hparams.get('num_local_experts'))
 
@@ -8912,13 +9377,16 @@ class GraniteHybridModel(Mamba2Model, GraniteMoeModel):
             name.endswith("block_sparse_moe.input_linear.weight")
             or "shared_mlp" in name
         ):
-            return GraniteMoeModel.modify_tensors(self, data_torch, name, bid)
+            yield from GraniteMoeModel.modify_tensors(self, data_torch, name, bid)
+            return
 
         # Determine whether this is a mamba layer or an attention layer
         if bid in self._ssm_layers:
-            return Mamba2Model.modify_tensors(self, data_torch, name, bid)
+            yield from Mamba2Model.modify_tensors(self, data_torch, name, bid)
+            return
         elif bid in self._attn_layers:
-            return GraniteMoeModel.modify_tensors(self, data_torch, name, bid)
+            yield from GraniteMoeModel.modify_tensors(self, data_torch, name, bid)
+            return
         yield from ModelBase.modify_tensors(self, data_torch, name, bid)
 
     def set_gguf_parameters(self):

convert_hf_to_gguf_update.py

@@ -148,6 +148,7 @@ models = [
     {"name": "youtu",            "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tencent/Youtu-LLM-2B", },
     {"name": "solar-open",       "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/upstage/Solar-Open-100B", },
     {"name": "exaone-moe",       "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LGAI-EXAONE/K-EXAONE-236B-A23B", },
+    {"name": "qwen35",           "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Qwen/Qwen3.5-9B-Instruct", }
 ]
 
 # some models are known to be broken upstream, so we will skip them as exceptions

docs/backend/SYCL.md

@@ -22,12 +22,11 @@
 - **DPCPP** *(Data Parallel C++)*: The primary oneAPI SYCL implementation, which includes the icpx/icx Compilers.
 - **oneAPI Libraries**: A set of highly optimized libraries targeting multiple domains *(e.g. Intel oneMKL, oneMath and oneDNN)*.
 - **oneAPI LevelZero**: A high performance low level interface for fine-grained control over Intel iGPUs and dGPUs.
-- **Nvidia & AMD Plugins**: These are plugins extending oneAPI's DPCPP support to SYCL on Nvidia and AMD GPU targets.
 
 ### Llama.cpp + SYCL
 
 The llama.cpp SYCL backend is primarily designed for **Intel GPUs**.
-SYCL cross-platform capabilities enable support for Nvidia GPUs as well, with limited support for AMD.
+SYCL cross-platform capabilities enable support for other vendor GPUs as well.
 
 ## Recommended Release
 
@@ -35,13 +34,16 @@ The following releases are verified and recommended:
 
 |Commit ID|Tag|Release|Verified  Platform| Update date|
 |-|-|-|-|-|
-|24e86cae7219b0f3ede1d5abdf5bf3ad515cccb8|b5377 |[llama-b5377-bin-win-sycl-x64.zip](https://github.com/ggml-org/llama.cpp/releases/download/b5377/llama-b5377-bin-win-sycl-x64.zip) |ArcB580/Linux/oneAPI 2025.1<br>LNL Arc GPU/Windows 11/oneAPI 2025.1.1|2025-05-15|
-|3bcd40b3c593d14261fb2abfabad3c0fb5b9e318|b4040 |[llama-b4040-bin-win-sycl-x64.zip](https://github.com/ggml-org/llama.cpp/releases/download/b4040/llama-b4040-bin-win-sycl-x64.zip) |Arc770/Linux/oneAPI 2024.1<br>MTL Arc GPU/Windows 11/oneAPI 2024.1| 2024-11-19|
-|fb76ec31a9914b7761c1727303ab30380fd4f05c|b3038 |[llama-b3038-bin-win-sycl-x64.zip](https://github.com/ggml-org/llama.cpp/releases/download/b3038/llama-b3038-bin-win-sycl-x64.zip) |Arc770/Linux/oneAPI 2024.1<br>MTL Arc GPU/Windows 11/oneAPI 2024.1||
+|24e86cae7219b0f3ede1d5abdf5bf3ad515cccb8|b5377 |[llama-b5377-bin-win-sycl-x64.zip](https://github.com/ggml-org/llama.cpp/releases/download/b5377/llama-b5377-bin-win-sycl-x64.zip) |Arc B580/Linux/oneAPI 2025.1<br>LNL Arc GPU/Windows 11/oneAPI 2025.1.1|2025-05-15|
+|3bcd40b3c593d14261fb2abfabad3c0fb5b9e318|b4040 |[llama-b4040-bin-win-sycl-x64.zip](https://github.com/ggml-org/llama.cpp/releases/download/b4040/llama-b4040-bin-win-sycl-x64.zip) |Arc A770/Linux/oneAPI 2024.1<br>MTL Arc GPU/Windows 11/oneAPI 2024.1| 2024-11-19|
+|fb76ec31a9914b7761c1727303ab30380fd4f05c|b3038 |[llama-b3038-bin-win-sycl-x64.zip](https://github.com/ggml-org/llama.cpp/releases/download/b3038/llama-b3038-bin-win-sycl-x64.zip) |Arc A770/Linux/oneAPI 2024.1<br>MTL Arc GPU/Windows 11/oneAPI 2024.1||
 
 
 ## News
 
+- 2026.02
+  - Remove support for Nvidia & AMD GPU, because the oneAPI plugin for Nvidia & AMD GPU is unavailable: download/installation channels are out of work. User can't build up the software for Nvidia & AMD GPU.
+
 - 2025.11
   - Support malloc memory on device more than 4GB.
 
@@ -51,7 +53,7 @@ The following releases are verified and recommended:
     |-|-|-|-|
     |PVC 1550|39|73|+87%|
     |Flex 170|39|50|+28%|
-    |Arc770|42|55|+30%|
+    |Arc A770|42|55|+30%|
     |MTL|13|16|+23%|
     |ARL-H|14|17|+21%|
 
@@ -62,7 +64,7 @@ The following releases are verified and recommended:
   - Use oneDNN as the default GEMM library, improve the compatibility for new Intel GPUs.
 
 - 2024.5
-  - Performance is increased: 34 -> 37 tokens/s of llama-2-7b.Q4_0 on Arc770.
+  - Performance is increased: 34 -> 37 tokens/s of llama-2-7b.Q4_0 on Arc A770.
   - Arch Linux is verified successfully.
 
 - 2024.4
@@ -111,14 +113,15 @@ On older Intel GPUs, you may try [OpenCL](/docs/backend/OPENCL.md) although the
 |-------------------------------|---------|---------------------------------------|
 | Intel Data Center Max Series  | Support | Max 1550, 1100                        |
 | Intel Data Center Flex Series | Support | Flex 170                              |
-| Intel Arc Series              | Support | Arc 770, 730M, Arc A750, B580         |
+| Intel Arc A-Series            | Support | Arc A770, Arc A730M, Arc A750         |
+| Intel Arc B-Series            | Support | Arc B580                              |
 | Intel built-in Arc GPU        | Support | built-in Arc GPU in Meteor Lake, Arrow Lake, Lunar Lake |
 | Intel iGPU                    | Support | iGPU in 13700k, 13400, i5-1250P, i7-1260P, i7-1165G7  |
 
 *Notes:*
 
 - **Memory**
-  - The device memory is a limitation when running a large model. The loaded model size, *`llm_load_tensors: buffer_size`*, is displayed in the log when running `./bin/llama-cli`.
+  - The device memory is a limitation when running a large model. The loaded model size, *`llm_load_tensors: buffer_size`*, is displayed in the log when running `./bin/llama-completion`.
   - Please make sure the GPU shared memory from the host is large enough to account for the model's size. For e.g. the *llama-2-7b.Q4_0* requires at least 8.0GB for integrated GPU and 4.0GB for discrete GPU.
 
 - **Execution Unit (EU)**
@@ -126,20 +129,7 @@ On older Intel GPUs, you may try [OpenCL](/docs/backend/OPENCL.md) although the
 
 ### Other Vendor GPU
 
-**Verified devices**
-
-| Nvidia GPU               | Status    | Verified Model |
-|--------------------------|-----------|----------------|
-| Ampere Series            | Supported | A100, A4000    |
-| Ampere Series *(Mobile)* | Supported | RTX 40 Series  |
-
-| AMD GPU                  | Status       | Verified Model |
-|--------------------------|--------------|----------------|
-| Radeon Pro               | Experimental | W6800          |
-| Radeon RX                | Experimental | 6700 XT        |
-
-Note: AMD GPU support is highly experimental and is incompatible with F16.
-Additionally, it only supports GPUs with a sub_group_size (warp size) of 32.
+NA
 
 ## Docker
 
@@ -148,11 +138,11 @@ The docker build option is currently limited to *Intel GPU* targets.
 ### Build image
 
 ```sh
-# Using FP16
-docker build -t llama-cpp-sycl --build-arg="GGML_SYCL_F16=ON" --target light -f .devops/intel.Dockerfile .
-
 # Using FP32
 docker build -t llama-cpp-sycl --build-arg="GGML_SYCL_F16=OFF" --target light -f .devops/intel.Dockerfile .
+
+# Using FP16
+docker build -t llama-cpp-sycl --build-arg="GGML_SYCL_F16=ON" --target light -f .devops/intel.Dockerfile .
 ```
 
 *Notes*:
@@ -211,14 +201,6 @@ Platform #0: Intel(R) OpenCL HD Graphics
  `-- Device #0: Intel(R) Iris(R) Xe Graphics [0x9a49]
 ```
 
-- **Nvidia GPU**
-
-In order to target Nvidia GPUs through SYCL, please make sure the CUDA/CUBLAS native requirements *-found [here](README.md#cuda)-* are installed.
-
-- **AMD GPU**
-
-To target AMD GPUs with SYCL, the ROCm stack must be installed first.
-
 2. **Install Intel® oneAPI Base toolkit**
 
 SYCL backend depends on:
@@ -247,23 +229,6 @@ Upon a successful installation, SYCL is enabled for the available intel devices,
 |2025.1|
 |2024.1|
 
-- **Adding support to Nvidia GPUs**
-
-**oneAPI Plugin**: In order to enable SYCL support on Nvidia GPUs, please install the [Codeplay oneAPI Plugin for Nvidia GPUs](https://developer.codeplay.com/products/oneapi/nvidia/download). User should also make sure the plugin version matches the installed base toolkit one *(previous step)* for a seamless "oneAPI on Nvidia GPU" setup.
-
-**oneDNN**: The current oneDNN releases *(shipped with the oneAPI base-toolkit)* do not include the NVIDIA backend. Therefore, oneDNN must be compiled from source to enable the NVIDIA target:
-
-```sh
-git clone https://github.com/oneapi-src/oneDNN.git
-cd oneDNN
-cmake -GNinja -Bbuild-nvidia -DDNNL_CPU_RUNTIME=DPCPP -DDNNL_GPU_RUNTIME=DPCPP -DDNNL_GPU_VENDOR=NVIDIA -DONEDNN_BUILD_GRAPH=OFF -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
-cmake --build build-nvidia --config Release
-```
-
-- **Adding support to AMD GPUs**
-
-**oneAPI Plugin**: In order to enable SYCL support on AMD GPUs, please install the [Codeplay oneAPI Plugin for AMD GPUs](https://developer.codeplay.com/products/oneapi/amd/download). As with Nvidia GPUs, the user should also make sure the plugin version matches the installed base toolkit.
-
 3. **Verify installation and environment**
 
 In order to check the available SYCL devices on the machine, please use the `sycl-ls` command.
@@ -284,25 +249,6 @@ When targeting an intel GPU, the user should expect one or more devices among th
 [opencl:gpu][opencl:2] Intel(R) OpenCL Graphics, Intel(R) UHD Graphics 730 OpenCL 3.0 NEO  [24.39.31294]
 ```
 
-- **Nvidia GPU**
-
-Similarly, user targeting Nvidia GPUs should expect at least one SYCL-CUDA device [`cuda:gpu`] as below:
-
-```
-[opencl:acc][opencl:0] Intel(R) FPGA Emulation Platform for OpenCL(TM), Intel(R) FPGA Emulation Device OpenCL 1.2  [2023.16.12.0.12_195853.xmain-hotfix]
-[opencl:cpu][opencl:1] Intel(R) OpenCL, Intel(R) Xeon(R) Gold 6326 CPU @ 2.90GHz OpenCL 3.0 (Build 0) [2023.16.12.0.12_195853.xmain-hotfix]
-[cuda:gpu][cuda:0] NVIDIA CUDA BACKEND, NVIDIA A100-PCIE-40GB 8.0 [CUDA 12.5]
-```
-
-- **AMD GPU**
-
-For AMD GPUs we should expect at least one SYCL-HIP device [`hip:gpu`]:
-
-```
-[opencl:cpu][opencl:0] Intel(R) OpenCL, 12th Gen Intel(R) Core(TM) i9-12900K OpenCL 3.0 (Build 0) [2024.18.6.0.02_160000]
-[hip:gpu][hip:0] AMD HIP BACKEND, AMD Radeon PRO W6800 gfx1030 [HIP 60140.9]
-```
-
 ### II. Build llama.cpp
 
 #### Intel GPU
@@ -331,47 +277,6 @@ It is possible to come across some precision issues when running tests that stem
 instructions, which can be circumvented by setting the environment variable `SYCL_PROGRAM_COMPILE_OPTIONS`
 as `-cl-fp32-correctly-rounded-divide-sqrt`
 
-#### Nvidia GPU
-
-The SYCL backend depends on [oneMath](https://github.com/uxlfoundation/oneMath) for Nvidia and AMD devices.
-By default it is automatically built along with the project. A specific build can be provided by setting the CMake flag `-DoneMath_DIR=/path/to/oneMath/install/lib/cmake/oneMath`.
-
-```sh
-# Build LLAMA with Nvidia BLAS acceleration through SYCL
-# Setting GGML_SYCL_DEVICE_ARCH is optional but can improve performance
-GGML_SYCL_DEVICE_ARCH=sm_80 # Example architecture
-
-# Option 1: Use FP32 (recommended for better performance in most cases)
-cmake -B build -DGGML_SYCL=ON -DGGML_SYCL_TARGET=NVIDIA -DGGML_SYCL_DEVICE_ARCH=${GGML_SYCL_DEVICE_ARCH} -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DDNNL_DIR=/path/to/oneDNN/build-nvidia/install/lib/cmake/dnnl
-
-# Option 2: Use FP16
-cmake -B build -DGGML_SYCL=ON -DGGML_SYCL_TARGET=NVIDIA -DGGML_SYCL_DEVICE_ARCH=${GGML_SYCL_DEVICE_ARCH} -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_SYCL_F16=ON -DDNNL_DIR=/path/to/oneDNN/build-nvidia/install/lib/cmake/dnnl
-
-# build all binary
-cmake --build build --config Release -j -v
-```
-
-It is possible to come across some precision issues when running tests that stem from using faster
-instructions, which can be circumvented by passing the `-fno-fast-math` flag to the compiler.
-
-#### AMD GPU
-
-The SYCL backend depends on [oneMath](https://github.com/uxlfoundation/oneMath) for Nvidia and AMD devices.
-By default it is automatically built along with the project. A specific build can be provided by setting the CMake flag `-DoneMath_DIR=/path/to/oneMath/install/lib/cmake/oneMath`.
-
-```sh
-# Build LLAMA with rocBLAS acceleration through SYCL
-
-## AMD
-# Use FP32, FP16 is not supported
-# Find your GGML_SYCL_DEVICE_ARCH with rocminfo, under the key 'Name:'
-GGML_SYCL_DEVICE_ARCH=gfx90a # Example architecture
-cmake -B build -DGGML_SYCL=ON -DGGML_SYCL_TARGET=AMD -DGGML_SYCL_DEVICE_ARCH=${GGML_SYCL_DEVICE_ARCH} -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
-
-# build all binary
-cmake --build build --config Release -j -v
-```
-
 ### III. Run the inference
 
 #### Retrieve and prepare model
@@ -422,16 +327,12 @@ Choose one of following methods to run.
 - Use device 0:
 
 ```sh
-./examples/sycl/run-llama2.sh 0
-# OR
-./examples/sycl/run-llama3.sh 0
+./examples/sycl/test.sh -mg 0
 ```
 - Use multiple devices:
 
 ```sh
-./examples/sycl/run-llama2.sh
-# OR
-./examples/sycl/run-llama3.sh
+./examples/sycl/test.sh
 ```
 
 2. Command line
@@ -454,13 +355,13 @@ Examples:
 - Use device 0:
 
 ```sh
-ZES_ENABLE_SYSMAN=1 ./build/bin/llama-cli -no-cnv -m models/llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 99 -sm none -mg 0
+ZES_ENABLE_SYSMAN=1 ./build/bin/llama-completion -no-cnv -m models/llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 99 -sm none -mg 0 --mmap
 ```
 
 - Use multiple devices:
 
 ```sh
-ZES_ENABLE_SYSMAN=1 ./build/bin/llama-cli -no-cnv -m models/llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 99 -sm layer
+ZES_ENABLE_SYSMAN=1 ./build/bin/llama-completion -no-cnv -m models/llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 99 -sm layer --mmap
 ```
 
 *Notes:*
@@ -576,13 +477,13 @@ Or, use CMake presets to build:
 
 ```sh
 cmake --preset x64-windows-sycl-release
-cmake --build build-x64-windows-sycl-release -j --target llama-cli
+cmake --build build-x64-windows-sycl-release -j --target llama-completion
 
 cmake -DGGML_SYCL_F16=ON --preset x64-windows-sycl-release
-cmake --build build-x64-windows-sycl-release -j --target llama-cli
+cmake --build build-x64-windows-sycl-release -j --target llama-completion
 
 cmake --preset x64-windows-sycl-debug
-cmake --build build-x64-windows-sycl-debug -j --target llama-cli
+cmake --build build-x64-windows-sycl-debug -j --target llama-completion
 ```
 
 #### 3. Visual Studio
@@ -607,7 +508,7 @@ You can use Visual Studio to open the `llama.cpp` folder directly as a CMake pro
 - For a minimal experimental setup, you can build only the inference executable using:
 
     ```Powershell
-    cmake --build build --config Release -j --target llama-cli
+    cmake --build build --config Release -j --target llama-completion
     ```
 
 ##### - Generating a Visual Studio Solution
@@ -713,13 +614,7 @@ Choose one of following methods to run.
 1. Script
 
 ```
-examples\sycl\win-run-llama-2.bat
-```
-
-or
-
-```
-examples\sycl\win-run-llama-3.bat
+examples\sycl\win-test.bat
 ```
 
 2. Command line
@@ -743,13 +638,13 @@ Examples:
 - Use device 0:
 
 ```
-build\bin\llama-cli.exe -no-cnv -m models\llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e -ngl 99 -sm none -mg 0
+build\bin\llama-completion.exe -no-cnv -m models\llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e -ngl 99 -sm none -mg 0 --mmap
 ```
 
 - Use multiple devices:
 
 ```
-build\bin\llama-cli.exe -no-cnv -m models\llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e -ngl 99 -sm layer
+build\bin\llama-completion.exe -no-cnv -m models\llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e -ngl 99 -sm layer --mmap
 ```
 
 
@@ -775,15 +670,15 @@ use 1 SYCL GPUs: [0] with Max compute units:512
 | Name               | Value                                 | Function                                    |
 |--------------------|---------------------------------------|---------------------------------------------|
 | GGML_SYCL          | ON (mandatory)                        | Enable build with SYCL code path.           |
-| GGML_SYCL_TARGET   | INTEL *(default)* \| NVIDIA \| AMD    | Set the SYCL target device type.            |
-| GGML_SYCL_DEVICE_ARCH | Optional (except for AMD)             | Set the SYCL device architecture, optional except for AMD. Setting the device architecture can improve the performance. See the table [--offload-arch](https://github.com/intel/llvm/blob/sycl/sycl/doc/design/OffloadDesign.md#--offload-arch) for a list of valid architectures. |
+| GGML_SYCL_TARGET   | INTEL *(default)*                     | Set the SYCL target device type.            |
+| GGML_SYCL_DEVICE_ARCH | Optional                           | Set the SYCL device architecture. Setting the device architecture can improve the performance. See the table [--offload-arch](https://github.com/intel/llvm/blob/sycl/sycl/doc/design/OffloadDesign.md#--offload-arch) for a list of valid architectures. |
 | GGML_SYCL_F16      | OFF *(default)* \|ON *(optional)*     | Enable FP16 build with SYCL code path. (1.) |
-| GGML_SYCL_GRAPH    | ON *(default)* \|OFF *(Optional)*     | Enable build with [SYCL Graph extension](https://github.com/intel/llvm/blob/sycl/sycl/doc/extensions/experimental/sycl_ext_oneapi_graph.asciidoc). |
+| GGML_SYCL_GRAPH    | OFF *(default)* \|ON *(Optional)*     | Enable build with [SYCL Graph extension](https://github.com/intel/llvm/blob/sycl/sycl/doc/extensions/experimental/sycl_ext_oneapi_graph.asciidoc). |
 | GGML_SYCL_DNN      | ON *(default)* \|OFF *(Optional)*     | Enable build with oneDNN.                   |
 | CMAKE_C_COMPILER   | `icx` *(Linux)*, `icx/cl` *(Windows)* | Set `icx` compiler for SYCL code path.      |
 | CMAKE_CXX_COMPILER | `icpx` *(Linux)*, `icx` *(Windows)*   | Set `icpx/icx` compiler for SYCL code path. |
 
-1. FP16 is recommended for better prompt processing performance on quantized models. Performance is equivalent in text generation but set `GGML_SYCL_F16=OFF` if you are experiencing issues with FP16 builds.
+1. FP32 or FP16 have different performance impact to LLM. Recommended to test them for better prompt processing performance on your models. You need to rebuild the code after change `GGML_SYCL_F16=OFF/ON`.
 
 #### Runtime
 
@@ -791,7 +686,7 @@ use 1 SYCL GPUs: [0] with Max compute units:512
 |-------------------|------------------|---------------------------------------------------------------------------------------------------------------------------|
 | GGML_SYCL_DEBUG   | 0 (default) or 1 | Enable log function by macro: GGML_SYCL_DEBUG                                                                             |
 | GGML_SYCL_DISABLE_OPT | 0 (default) or 1 | Disable optimize features for Intel GPUs. (Recommended to 1 for intel devices older than Gen 10) |
-| GGML_SYCL_DISABLE_GRAPH | 0 or 1 (default) | Disable running computations through SYCL Graphs feature. Disabled by default because graph performance isn't yet better than non-graph performance. |
+| GGML_SYCL_DISABLE_GRAPH | 0 or 1 (default) | Disable running computations through SYCL Graphs feature. Disabled by default because SYCL Graph is still on development, no better performance. |
 | GGML_SYCL_DISABLE_DNN | 0 (default) or 1 | Disable running computations through oneDNN and always use oneMKL. |
 | ZES_ENABLE_SYSMAN | 0 (default) or 1 | Support to get free memory of GPU by sycl::aspect::ext_intel_free_memory.<br>Recommended to use when --split-mode = layer |
 | UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS | 0 (default) or 1 | Support malloc device memory more than 4GB.|

docs/backend/VirtGPU.md

@@ -0,0 +1,180 @@
+# GGML-VirtGPU Backend
+
+The GGML-VirtGPU backend enables GGML applications to run machine
+learning computations on host hardware while the application itself
+runs inside a virtual machine.  It uses host-guest shared memory to
+efficiently share data buffers between the two sides.
+
+This backend relies on the virtio-gpu, and VirglRenderer API Remoting
+(APIR) component. The backend is split into two libraries:
+- a GGML implementation (the "remoting frontend"), running in the
+  guest and interacting with the virtgpu device
+- a VirglRenderer APIR compatible library (the "remoting backend"),
+  running in the host and interacting with Virglrenderer and an actual
+  GGML device backend.
+
+## OS support
+
+| OS       | Status            | Backend     | CI testing  | Notes
+| -------- | ----------------- | ----------- | ----------- | -----
+| MacOS 14 | Supported         | ggml-metal  | X           | Working when compiled on MacOS 14
+| MacOS 15 | Supported         | ggml-metal  | X           | Working when compiled on MacOS 14 or MacOS 15
+| MacOS 26 | Not tested        |             |             |
+| Linux    | Under development | ggml-vulkan | not working | Working locally, CI running into deadlocks
+
+
+## Architecture Overview
+
+The GGML-VirtGPU backend consists of three main components:
+
+```mermaid
+graph TD
+    %% Nodes
+
+ subgraph GuestVM ["Guest VM - Frontend"]
+        App([GGML Application<br/>llama.cpp, etc.])
+
+        direction TB
+        Interface[GGML Backend Interface]
+        Comm["GGML-VirtGPU<br/>(hypercalls + shared mem)"]
+
+        App --> Interface
+        Interface --> Comm
+    end
+
+    API[virtio-gpu / virglrenderer API]
+
+    subgraph HostSystem [Host System - Backend]
+        direction TB
+        Dispatcher[GGML-VirtGPU-Backend]
+        BackendLib[GGML Backend library<br/>Metal / Vulkan / CPU / ...]
+
+        Dispatcher --> BackendLib
+    end
+
+    %% Connections
+    Comm --> API
+    API --> HostSystem
+```
+
+### Key Components
+
+1. **Guest-side Frontend** (`ggml-virtgpu/`): Implements the GGML backend interface and forwards operations to the host
+2. **Host-side Backend** (`ggml-virtgpu/backend/`): Receives forwarded operations and executes them on actual hardware backends
+3. **Communication Layer**: Uses virtio-gpu hypercalls and shared memory for efficient data transfer
+
+## Features
+
+- **Dynamic backend loading** on the host side (CPU, CUDA, Metal, etc.)
+- **Zero-copy data transfer** via host-guest shared memory pages
+
+## Communication Protocol
+
+### Hypercalls and Shared Memory
+
+The backend uses two primary communication mechanisms:
+
+1. **Hypercalls (`DRM_IOCTL_VIRTGPU_EXECBUFFER`)**: Trigger remote execution from guest to host
+2. **Shared Memory Pages**: Zero-copy data transfer for tensors and parameters
+
+#### Shared Memory Layout
+
+Each connection uses two shared memory buffers:
+
+- **Data Buffer** (24 MiB): For command/response data and tensor transfers
+- **Reply Buffer** (16 KiB): For command replies and status information
+- **Data Buffers**: Dynamically allocated host-guest shared buffers
+  served as GGML buffers.
+
+### APIR Protocol
+
+The Virglrender API Remoting protocol defines three command types:
+
+- `HANDSHAKE`: Protocol version negotiation and capability discovery
+- `LOADLIBRARY`: Dynamic loading of backend libraries on the host
+- `FORWARD`: API function call forwarding
+
+### Binary Serialization
+
+Commands and data are serialized using a custom binary protocol with:
+
+- Fixed-size encoding for basic types
+- Variable-length arrays with size prefixes
+- Buffer bounds checking
+- Error recovery mechanisms
+
+## Supported Operations
+
+### Device Operations
+- Device enumeration and capability queries
+- Memory information (total/free)
+- Backend type detection
+
+### Buffer Operations
+- Buffer allocation and deallocation
+- Tensor data transfer (host ↔ guest)
+- Memory copying and clearing
+
+### Computation Operations
+- Graph execution forwarding
+
+## Build Requirements
+
+### Guest-side Dependencies
+- `libdrm` for DRM/virtio-gpu communication
+- C++20 compatible compiler
+- CMake 3.14+
+
+### Host-side Dependencies
+- virglrenderer with APIR support (pending upstream review)
+- Target backend libraries (libggml-metal, libggml-vulkan, etc.)
+
+## Configuration
+
+### Environment Variables
+
+- `GGML_VIRTGPU_BACKEND_LIBRARY`: Path to the host-side backend library
+- `GGML_VIRTGPU_DEBUG`: Enable debug logging
+
+### Build Options
+
+- `GGML_VIRTGPU`: Enable the VirtGPU backend (`ON` or `OFF`, default: `OFF`)
+- `GGML_VIRTGPU_BACKEND`: Build the host-side backend component (`ON`, `OFF` or `ONLY`, default: `OFF`)
+
+### System Requirements
+
+- VM with virtio-gpu support
+- VirglRenderer with APIR patches
+- Compatible backend libraries on host
+
+## Limitations
+
+- **VM-specific**: Only works in virtual machines with virtio-gpu support
+- **Host dependency**: Requires properly configured host-side backend
+- **Latency**: Small overhead from VM escaping for each operation
+
+
+* This work is pending upstream changes in the VirglRenderer
+  project.
+  * The backend can be tested with Virglrenderer compiled from source
+  using this PR:
+  https://gitlab.freedesktop.org/virgl/virglrenderer/-/merge_requests/1590
+* This work is pending changes in the VMM/hypervisor running the
+  virtual machine, which need to know how to route the newly
+  introduced APIR capset.
+  * The environment variable `VIRGL_ROUTE_VENUS_TO_APIR=1` allows
+    using the Venus capset, until the relevant hypervisors have been
+    patched. However, setting this flag breaks the Vulkan/Venus normal
+    behavior.
+  * The environment variable `GGML_REMOTING_USE_APIR_CAPSET` tells the
+    `ggml-virtgpu` backend to use the APIR capset. This will become
+    the default when the relevant hypervisors have been patched.
+
+* This work focused on improving the performance of llama.cpp running
+  on MacOS containers, and is mainly tested on this platform. The
+  linux support (via `krun`) is in progress.
+
+## See Also
+
+- [Development and Testing](VirtGPU/development.md)
+- [Backend configuration](VirtGPU/configuration.md)

docs/backend/VirtGPU/configuration.md

@@ -0,0 +1,174 @@
+# GGML-VirtGPU Backend Configuration
+
+This document describes the environment variables used by the ggml-virtgpu backend system, covering both the frontend (guest-side) and backend (host-side) components.
+
+## Environment Variables Overview
+
+The ggml-virtgpu backend uses environment variables for configuration across three main components:
+- **Frontend (Guest)**: GGML applications running in VMs
+- **Hypervisor**: Virglrenderer/APIR system
+- **Backend (Host)**: Host-side GGML backend integration
+
+## Frontend (Guest-side) Configuration
+
+### GGML_REMOTING_USE_APIR_CAPSET
+- **Location**: `ggml/src/ggml-virtgpu/virtgpu.cpp`
+- **Type**: Boolean flag (presence-based)
+- **Purpose**: Controls which virtio-gpu capability set to use for communication
+- **Values**:
+  - Set (any value): Use the APIR capset (long-term setup)
+  - Unset: Use the Venus capset (easier for testing with an unmodified hypervisor)
+- **Default**: Unset (Venus capset)
+- **Usage**:
+  ```bash
+  export GGML_REMOTING_USE_APIR_CAPSET=1  # Use APIR capset
+  # or leave unset for Venus capset
+  ```
+
+## Hypervisor (Virglrenderer/APIR) Configuration
+
+These environment variables are used during the transition phase for
+running with an unmodified hypervisor (not supporting the
+VirglRenderer APIR component). They will be removed in the future, and
+the hypervisor will instead configure VirglRenderer with the APIR
+_Configuration Key_.
+
+### VIRGL_APIR_BACKEND_LIBRARY
+- **Location**: `virglrenderer/src/apir/apir-context.c`
+- **Configuration Key**: `apir.load_library.path`
+- **Type**: File path string
+- **Purpose**: Path to the APIR backend library that virglrenderer should dynamically load
+- **Required**: Yes
+- **Example**:
+  ```bash
+  export VIRGL_APIR_BACKEND_LIBRARY="/path/to/libggml-remotingbackend.so"
+  ```
+
+### VIRGL_ROUTE_VENUS_TO_APIR
+- **Location**: `virglrenderer/src/apir/apir-renderer.h`
+- **Type**: Boolean flag (presence-based)
+- **Purpose**: Temporary workaround to route Venus capset calls to APIR during hypervisor transition period
+- **Status**: will be removed once hypervisors support APIR natively
+- **Warning**: Breaks normal Vulkan/Venus functionality
+- **Usage**:
+  ```bash
+  export VIRGL_ROUTE_VENUS_TO_APIR=1  # For testing with an unmodified hypervisor
+  ```
+
+### VIRGL_APIR_LOG_TO_FILE
+- **Location**: `virglrenderer/src/apir/apir-renderer.c`
+- **Environment Variable**: `VIRGL_APIR_LOG_TO_FILE`
+- **Type**: File path string
+- **Purpose**: Enable debug logging from the VirglRenderer APIR component to specified file
+- **Required**: No (optional debugging)
+- **Default**: Logging to `stderr`
+- **Usage**:
+  ```bash
+  export VIRGL_APIR_LOG_TO_FILE="/tmp/apir-debug.log"
+  ```
+
+## Backend (Host-side) Configuration
+
+These environment variables are used during the transition phase for
+running with an unmodified hypervisor (not supporting the
+VirglRenderer APIR component). They will be removed in the future, and
+the hypervisor will instead configure VirglRenderer with the APIR
+_Configuration Key_.
+
+### APIR_LLAMA_CPP_GGML_LIBRARY_PATH
+- **Location**: `ggml/src/ggml-virtgpu/backend/backend.cpp`
+- **Environment Variable**: `APIR_LLAMA_CPP_GGML_LIBRARY_PATH`
+- **Configuration Key**: `ggml.library.path`
+- **Type**: File path string
+- **Purpose**: Path to the actual GGML backend library (Metal, CUDA, Vulkan, etc.)
+- **Required**: **Yes** - backend initialization fails without this
+- **Examples**:
+  ```bash
+  # macOS with Metal backend
+  export APIR_LLAMA_CPP_GGML_LIBRARY_PATH="/opt/llama.cpp/lib/libggml-metal.dylib"
+
+  # Linux with CUDA backend
+  export APIR_LLAMA_CPP_GGML_LIBRARY_PATH="/opt/llama.cpp/lib/libggml-cuda.so"
+
+  # macOS or Linux with Vulkan backend
+  export APIR_LLAMA_CPP_GGML_LIBRARY_PATH="/opt/llama.cpp/lib/libggml-vulkan.so"
+  ```
+
+### APIR_LLAMA_CPP_GGML_LIBRARY_REG
+- **Location**: `ggml/src/ggml-virtgpu/backend/backend.cpp`
+- **Environment Variable**: `APIR_LLAMA_CPP_GGML_LIBRARY_REG`
+- **Configuration Key**: `ggml.library.reg`
+- **Type**: Function symbol name string
+- **Purpose**: Name of the backend registration function to call after loading the library
+- **Required**: No (defaults to `ggml_backend_init`)
+- **Default**: `ggml_backend_init`
+- **Examples**:
+  ```bash
+  # Metal backend
+  export APIR_LLAMA_CPP_GGML_LIBRARY_REG="ggml_backend_metal_reg"
+
+  # CUDA backend
+  export APIR_LLAMA_CPP_GGML_LIBRARY_REG="ggml_backend_cuda_reg"
+
+  # Vulkan backend
+  export APIR_LLAMA_CPP_GGML_LIBRARY_REG="ggml_backend_vulkan_reg"
+
+  # Generic fallback (default)
+  # export APIR_LLAMA_CPP_GGML_LIBRARY_REG="ggml_backend_init"
+  ```
+
+### APIR_LLAMA_CPP_LOG_TO_FILE
+- **Location**: `ggml/src/ggml-virtgpu/backend/backend.cpp:62`
+- **Environment Variable**: `APIR_LLAMA_CPP_LOG_TO_FILE`
+- **Type**: File path string
+- **Purpose**: Enable debug logging from the GGML backend to specified file
+- **Required**: No (optional debugging)
+- **Usage**:
+  ```bash
+  export APIR_LLAMA_CPP_LOG_TO_FILE="/tmp/ggml-backend-debug.log"
+  ```
+
+## Configuration Flow
+
+The configuration system works as follows:
+
+1. **Hypervisor Setup**: Virglrenderer loads the APIR backend library specified by `VIRGL_APIR_BACKEND_LIBRARY`
+
+2. **Context Creation**: When an APIR context is created, it populates a configuration table with environment variables:
+   - `apir.load_library.path` ← `VIRGL_APIR_BACKEND_LIBRARY`
+   - `ggml.library.path` ← `APIR_LLAMA_CPP_GGML_LIBRARY_PATH`
+   - `ggml.library.reg` ← `APIR_LLAMA_CPP_GGML_LIBRARY_REG`
+   - this step will eventually be performed by the hypervisor itself, with command-line arguments instead of environment variables.
+
+3. **Backend Initialization**: The backend queries the configuration via callbacks:
+   - `virgl_cbs->get_config(ctx_id, "ggml.library.path")` returns the library path
+   - `virgl_cbs->get_config(ctx_id, "ggml.library.reg")` returns the registration function
+
+4. **Library Loading**: The backend dynamically loads and initializes the specified GGML library
+
+## Error Messages
+
+Common error scenarios and their messages:
+
+- **Missing library path**: `"cannot open the GGML library: env var 'APIR_LLAMA_CPP_GGML_LIBRARY_PATH' not defined"`
+- **Missing registration function**: `"cannot register the GGML library: env var 'APIR_LLAMA_CPP_GGML_LIBRARY_REG' not defined"`
+
+## Example Complete Configuration
+
+Here's an example configuration for a macOS host with Metal backend:
+
+```bash
+# Hypervisor environment
+export VIRGL_APIR_BACKEND_LIBRARY="/opt/llama.cpp/lib/libggml-virtgpu-backend.dylib"
+
+# Backend configuration
+export APIR_LLAMA_CPP_GGML_LIBRARY_PATH="/opt/llama.cpp/lib/libggml-metal.dylib"
+export APIR_LLAMA_CPP_GGML_LIBRARY_REG="ggml_backend_metal_reg"
+
+# Optional logging
+export VIRGL_APIR_LOG_TO_FILE="/tmp/apir.log"
+export APIR_LLAMA_CPP_LOG_TO_FILE="/tmp/ggml.log"
+
+# Guest configuration
+export GGML_REMOTING_USE_APIR_CAPSET=1
+```

docs/backend/VirtGPU/development.md

@@ -0,0 +1,220 @@
+# Development and Testing
+
+## Development
+
+### Code Generation
+
+The backend uses code generation from YAML configuration:
+
+```bash
+# Regenerate protocol code
+cd ggml-virtgpu/
+python regenerate_remoting.py
+```
+
+### Adding New Operations
+
+1. Add function definition to `ggmlremoting_functions.yaml`
+2. Regenerate code with `regenerate_remoting.py`
+3. Implement guest-side forwarding in `virtgpu-forward-*.cpp`
+4. Implement host-side handling in `backend-dispatched-*.cpp`
+
+## Testing
+
+This document provides instructions for building and testing the GGML-VirtGPU backend on macOS with containers.
+
+### Prerequisites
+
+The testing setup requires:
+
+- macOS host system
+- Container runtime with `libkrun` provider (podman machine)
+- Access to development patchset for VirglRenderer
+
+### Required Patchsets
+
+The backend requires patches that are currently under review:
+
+- **Virglrenderer APIR upstream PR**: https://gitlab.freedesktop.org/virgl/virglrenderer/-/merge_requests/1590 (for reference)
+- **MacOS Virglrenderer (for krunkit)**: https://gitlab.freedesktop.org/kpouget/virglrenderer/-/tree/main-macos
+- **Linux Virglrenderer (for krun)**: https://gitlab.freedesktop.org/kpouget/virglrenderer/-/tree/main-linux
+
+### Build Instructions
+
+#### 1. Build ggml-virtgpu-backend (Host-side, macOS)
+
+```bash
+# Build the backend that runs natively on macOS
+mkdir llama.cpp
+cd llama.cpp
+git clone https://github.com/ggml-org/llama.cpp.git src
+cd src
+
+LLAMA_MAC_BUILD=$PWD/build/ggml-virtgpu-backend
+
+cmake -S . -B $LLAMA_MAC_BUILD \
+      -DGGML_NATIVE=OFF \
+      -DLLAMA_CURL=ON \
+      -DGGML_REMOTINGBACKEND=ONLY \
+      -DGGML_METAL=ON
+
+TARGETS="ggml-metal"
+cmake --build $LLAMA_MAC_BUILD --parallel 8 --target $TARGETS
+
+# Build additional tools for native benchmarking
+EXTRA_TARGETS="llama-run llama-bench"
+cmake --build $LLAMA_MAC_BUILD --parallel 8 --target $EXTRA_TARGETS
+```
+
+#### 2. Build virglrenderer (Host-side, macOS)
+
+```bash
+# Build virglrenderer with APIR support
+mkdir virglrenderer
+git clone https://gitlab.freedesktop.org/kpouget/virglrenderer -b main-macos src
+cd src
+
+VIRGL_BUILD_DIR=$PWD/build
+
+# -Dvenus=true and VIRGL_ROUTE_VENUS_TO_APIR=1 route the APIR requests via the Venus backend, for easier testing without a patched hypervisor
+
+meson setup $VIRGL_BUILD_DIR \
+      -Dvenus=true \
+      -Dapir=true
+
+ninja -C $VIRGL_BUILD_DIR
+```
+
+#### 3. Build ggml-virtgpu (Guest-side, Linux)
+
+Option A: Build from a script:
+
+```bash
+# Inside a Linux container
+mkdir llama.cpp
+git clone https://github.com/ggml-org/llama.cpp.git src
+cd src
+
+LLAMA_LINUX_BUILD=$PWD//build-virtgpu
+
+cmake -S . -B $LLAMA_LINUX_BUILD \
+      -DGGML_VIRTGPU=ON
+
+ninja -C $LLAMA_LINUX_BUILD
+```
+
+Option B: Build container image with frontend:
+
+```bash
+cat << EOF > remoting.containerfile
+FROM quay.io/fedora/fedora:43
+USER 0
+
+WORKDIR /app/remoting
+
+ARG LLAMA_CPP_REPO="https://github.com/ggml-org/llama.cpp.git"
+ARG LLAMA_CPP_VERSION="master"
+ARG LLAMA_CPP_CMAKE_FLAGS="-DGGML_VIRTGPU=ON"
+ARG LLAMA_CPP_CMAKE_BUILD_FLAGS="--parallel 4"
+
+RUN dnf install -y git cmake gcc gcc-c++ libcurl-devel libdrm-devel
+
+RUN git clone "\${LLAMA_CPP_REPO}" src \\
+ && git -C src fetch origin \${LLAMA_CPP_VERSION} \\
+ && git -C src reset --hard FETCH_HEAD
+
+RUN mkdir -p build \\
+ && cd src \\
+ && set -o pipefail \\
+ && cmake -S . -B ../build \${LLAMA_CPP_CMAKE_FLAGS} \\
+ && cmake --build ../build/ \${LLAMA_CPP_CMAKE_BUILD_FLAGS}
+
+ENTRYPOINT ["/app/remoting/src/build/bin/llama-server"]
+EOF
+
+mkdir -p empty_dir
+podman build -f remoting.containerfile ./empty_dir -t localhost/llama-cpp.virtgpu
+```
+
+### Environment Setup
+
+#### Set krunkit Environment Variables
+
+```bash
+# Define the base directories (adapt these paths to your system)
+VIRGL_BUILD_DIR=$HOME/remoting/virglrenderer/build
+LLAMA_MAC_BUILD=$HOME/remoting/llama.cpp/build-backend
+
+# For krunkit to load the custom virglrenderer library
+export DYLD_LIBRARY_PATH=$VIRGL_BUILD_DIR/src
+
+# For Virglrenderer to load the ggml-remotingbackend library
+export VIRGL_APIR_BACKEND_LIBRARY="$LLAMA_MAC_BUILD/bin/libggml-virtgpu-backend.dylib"
+
+# For llama.cpp remotingbackend to load the ggml-metal backend
+export APIR_LLAMA_CPP_GGML_LIBRARY_PATH="$LLAMA_MAC_BUILD/bin/libggml-metal.dylib"
+export APIR_LLAMA_CPP_GGML_LIBRARY_REG=ggml_backend_metal_reg
+```
+
+#### Launch Container Environment
+
+```bash
+# Set container provider to libkrun
+export CONTAINERS_MACHINE_PROVIDER=libkrun
+podman machine start
+```
+
+#### Verify Environment
+
+Confirm that krunkit is using the correct virglrenderer library:
+
+```bash
+lsof -c krunkit | grep virglrenderer
+# Expected output:
+# krunkit 50574 user  txt  REG  1,14  2273912  10849442 ($VIRGL_BUILD_DIR/src)/libvirglrenderer.1.dylib
+```
+
+### Running Tests
+
+#### Launch Test Container
+
+```bash
+# Optional model caching
+mkdir -p models
+PODMAN_CACHE_ARGS="-v models:/models --user root:root --cgroupns host --security-opt label=disable -w /models"
+
+podman run $PODMAN_CACHE_ARGS -it --rm --device /dev/dri localhost/llama-cpp.virtgpu
+```
+
+#### Test llama.cpp in Container
+
+```bash
+
+# Run performance benchmark
+/app/remoting/build/bin/llama-bench -m ./llama3.2
+```
+
+Expected output (performance may vary):
+```
+| model                          |       size |     params | backend    | ngl |          test |                  t/s |
+| ------------------------------ | ---------: | ---------: | ---------- | --: | ------------: | -------------------: |
+| llama 3B Q4_K - Medium         |   1.87 GiB |     3.21 B | ggml-virtgpu |  99 |         pp512 |        991.30 ± 0.66 |
+| llama 3B Q4_K - Medium         |   1.87 GiB |     3.21 B | ggml-virtgpu |  99 |         tg128 |         85.71 ± 0.11 |
+```
+
+### Troubleshooting
+
+#### SSH Environment Variable Issues
+
+⚠️ **Warning**: Setting `DYLD_LIBRARY_PATH` from SSH doesn't work on macOS. Here is a workaround:
+
+**Workaround 1: Replace system library**
+```bash
+VIRGL_BUILD_DIR=$HOME/remoting/virglrenderer/build  # ⚠️ adapt to your system
+BREW_VIRGL_DIR=/opt/homebrew/Cellar/virglrenderer/0.10.4d/lib
+VIRGL_LIB=libvirglrenderer.1.dylib
+
+cd $BREW_VIRGL_DIR
+mv $VIRGL_LIB ${VIRGL_LIB}.orig
+ln -s $VIRGL_BUILD_DIR/src/$VIRGL_LIB
+```

docs/backend/snapdragon/CMakeUserPresets.json

@@ -1,5 +1,5 @@
 {
-  "version": 4,
+  "version": 5,
   "configurePresets": [
     {
         "name": "arm64-android-snapdragon",
@@ -16,7 +16,9 @@
             "CMAKE_CXX_FLAGS_RELEASE":        "-O3 -DNDEBUG",
             "CMAKE_C_FLAGS_RELWITHDEBINFO":   "-O3 -DNDEBUG -g",
             "CMAKE_CXX_FLAGS_RELWITHDEBINFO": "-O3 -DNDEBUG -g",
-            "HEXAGON_SDK_ROOT": "$env{HEXAGON_SDK_ROOT}",
+            "CMAKE_PREFIX_PATH":  "$env{OPENCL_SDK_ROOT}",
+            "HEXAGON_SDK_ROOT":   "$env{HEXAGON_SDK_ROOT}",
+            "HEXAGON_TOOLS_ROOT": "$env{HEXAGON_TOOLS_ROOT}",
             "PREBUILT_LIB_DIR": "android_aarch64",
             "GGML_OPENMP":      "OFF",
             "GGML_LLAMAFILE":   "OFF",
@@ -31,7 +33,15 @@
         "name": "arm64-windows-snapdragon",
         "inherits": [ "base", "arm64-windows-llvm" ],
         "cacheVariables": {
-            "HEXAGON_SDK_ROOT": "$env{HEXAGON_SDK_ROOT}",
+            "CMAKE_C_FLAGS":   "-march=armv8.7a+fp16 -fvectorize -ffp-model=fast -flto -D_GNU_SOURCE",
+            "CMAKE_CXX_FLAGS": "-march=armv8.7a+fp16 -fvectorize -ffp-model=fast -flto -D_GNU_SOURCE",
+            "CMAKE_C_FLAGS_RELEASE":          "-O3 -DNDEBUG",
+            "CMAKE_CXX_FLAGS_RELEASE":        "-O3 -DNDEBUG",
+            "CMAKE_C_FLAGS_RELWITHDEBINFO":   "-O3 -DNDEBUG -g",
+            "CMAKE_CXX_FLAGS_RELWITHDEBINFO": "-O3 -DNDEBUG -g",
+            "CMAKE_PREFIX_PATH":  "$env{OPENCL_SDK_ROOT}",
+            "HEXAGON_SDK_ROOT":   "$env{HEXAGON_SDK_ROOT}",
+            "HEXAGON_TOOLS_ROOT": "$env{HEXAGON_TOOLS_ROOT}",
             "PREBUILT_LIB_DIR": "windows_aarch64",
             "GGML_OPENMP":      "OFF",
             "GGML_LLAMAFILE":   "OFF",

docs/backend/snapdragon/README.md

@@ -1,6 +1,8 @@
-# Snapdragon-based Android devices
+# Snapdragon-based devices
 
-## How to Build
+## Setup
+
+### Android
 
 The easiest way to build llama.cpp for a Snapdragon-based Android device is using the toolchain Docker image (see github.com/snapdragon-toolchain).
 This image includes Android NDK, OpenCL SDK, Hexagon SDK, CMake, etc.
@@ -12,7 +14,24 @@ This method works on Linux, macOS, and Windows. macOS and Windows users should i
 [d]/> cd /workspace
 ```
 
-The rest of the Android build process assumes that you're running inside the toolchain container.
+Note: The rest of the **Android** build process assumes that you're running inside the toolchain container.
+
+### Windows On Snapdragon
+
+Native Windows 11 arm64 builds has the following tools dependencies:
+- MS Visual Studio 2026 (Community Edition or Pro)
+  - MSVC arm64 standard and runtime libraries
+  - UCRT and Driver Kit
+- LLVM core libraries and Clang compiler (winget)
+- CMake, Git, Python (winget)
+- Hexagon SDK Community Edition 6.4 or later (see windows.md)
+- OpenCL SDK 2.3 or later (see windows.md)
+
+Note: The rest of the **Windows** build process assumes that you're running natively in Powershell.
+Adapt below build commands accordingly.
+
+## How to Build
+
 Let's build llama.cpp with CPU, OpenCL, and Hexagon backends via CMake presets:
 
 ```
@@ -49,24 +68,26 @@ Preset CMake variables:
 To generate an installable "package" simply use cmake --install:
 
 ```
-[d]/workspace> cmake --install build-snapdragon --prefix pkg-adb/llama.cpp
+[d]/workspace> cmake --install build-snapdragon --prefix pkg-snapdragon/llama.cpp
 -- Install configuration: "Release"
--- Installing: /workspace/pkg-adb/llama.cpp/lib/libggml-cpu.so
--- Installing: /workspace/pkg-adb/llama.cpp/lib/libggml-opencl.so
--- Installing: /workspace/pkg-adb/llama.cpp/lib/libggml-hexagon.so
--- Installing: /workspace/pkg-adb/llama.cpp/lib/libggml-htp-v73.so
--- Installing: /workspace/pkg-adb/llama.cpp/lib/libggml-htp-v75.so
--- Installing: /workspace/pkg-adb/llama.cpp/lib/libggml-htp-v79.so
--- Installing: /workspace/pkg-adb/llama.cpp/lib/libggml-htp-v81.so
--- Installing: /workspace/pkg-adb/llama.cpp/lib/libggml.so
+-- Installing: /workspace/pkg-snapdragon/llama.cpp/lib/libggml-cpu.so
+-- Installing: /workspace/pkg-snapdragon/llama.cpp/lib/libggml-opencl.so
+-- Installing: /workspace/pkg-snapdragon/llama.cpp/lib/libggml-hexagon.so
+-- Installing: /workspace/pkg-snapdragon/llama.cpp/lib/libggml-htp-v73.so
+-- Installing: /workspace/pkg-snapdragon/llama.cpp/lib/libggml-htp-v75.so
+-- Installing: /workspace/pkg-snapdragon/llama.cpp/lib/libggml-htp-v79.so
+-- Installing: /workspace/pkg-snapdragon/llama.cpp/lib/libggml-htp-v81.so
+-- Installing: /workspace/pkg-snapdragon/llama.cpp/lib/libggml.so
 ...
--- Installing: /workspace/pkg-adb/llama.cpp/bin/llama-bench
--- Installing: /workspace/pkg-adb/llama.cpp/bin/llama-cli
+-- Installing: /workspace/pkg-snapdragon/llama.cpp/bin/llama-bench
+-- Installing: /workspace/pkg-snapdragon/llama.cpp/bin/llama-cli
 ...
 ```
 
 ## How to Install
 
+### Android
+
 For this step, your device needs to be configured for on-device development.
 Please see https://developer.android.com/studio/debug/dev-options for details.
 
@@ -74,10 +95,10 @@ Once ADB is enabled, use `adb push` to install `pkg-snapdragon` on the device.
 **Note that the toolchain Docker image doesn't have ADB and doesn't set up the ADB bridge. Please use native ADB on the host.**
 
 ```
-~/src/llama.cpp$ adb push pkg-adb/llama.cpp /data/local/tmp/
-pkg-adb/llama.cpp/bin/: 67 files pushed, 0 skipped. 190.2 MB/s (919095042 bytes in 4.607s)
-pkg-adb/llama.cpp/include/: 19 files pushed, 0 skipped. 20.5 MB/s (255173 bytes in 0.012s)
-pkg-adb/llama.cpp/lib/: 16 files pushed, 0 skipped. 144.4 MB/s (43801382 bytes in 0.289s)
+~/src/llama.cpp$ adb push pkg-snapdragon/llama.cpp /data/local/tmp/
+pkg-snapdragon/llama.cpp/bin/: 67 files pushed, 0 skipped. 190.2 MB/s (919095042 bytes in 4.607s)
+pkg-snapdragon/llama.cpp/include/: 19 files pushed, 0 skipped. 20.5 MB/s (255173 bytes in 0.012s)
+pkg-snapdragon/llama.cpp/lib/: 16 files pushed, 0 skipped. 144.4 MB/s (43801382 bytes in 0.289s)
 102 files pushed, 0 skipped. 186.9 MB/s (963151597 bytes in 4.914s)
 ```
 
@@ -92,6 +113,11 @@ At this point, you should also install some models:
 Llama-3.2-1B-Instruct-Q4_0.gguf: 1 file pushed, 0 skipped. 38.3 MB/s (773025920 bytes in 19.250s)
 ```
 
+### Windows
+
+All artifacts are already installed in the `pkg-snapdragon` folder.
+To run, adapt below instructions to use Powershell scrits in `scripts/snapdragon/windows`.
+
 ## How to Run
 
 The easiest way to run llama.cpp cli tools is using provided wrapper scripts that properly set up all required environment variables.

docs/backend/snapdragon/windows.md

@@ -0,0 +1,161 @@
+## Overview
+
+The document covers procedures for installing the latest GPU and NPU drivers, and OpenCL and Hexagon SDKs.
+
+
+In order to use Hexagon NPU on Snapdragon Windows devices the underlying HTP Ops libraries (e.g libggml-htp-v73.so)
+must be included in the .cat file digitally signed with a trusted certificate.
+
+This document covers details on how to generate personal certificate files (.pfx) and how to configure the system
+to allow for test signatures (aka test-signing).
+
+## Install the latest Adreno OpenCL SDK
+
+Either use the trimmed down version (optimized for CI) from
+
+    https://github.com/snapdragon-toolchain/opencl-sdk/releases/download/v2.3.2/adreno-opencl-sdk-v2.3.2-arm64-wos.tar.xz
+
+Or download the complete official version from
+
+    https://softwarecenter.qualcomm.com/catalog/item/Adreno_OpenCL_SDK?version=2.3.2
+
+Unzip/untar the archive into
+```
+c:\Qualcomm\OpenCL_SDK\2.3.2
+```
+
+## Install the latest Hexagon SDK Community Edition
+
+Either use the trimmed down version (optimized for CI) from
+
+    https://github.com/snapdragon-toolchain/hexagon-sdk/releases/download/v6.4.0.2/hexagon-sdk-v6.4.0.2-arm64-wos.tar.xz
+
+Or download the complete official version from
+
+    https://softwarecenter.qualcomm.com/catalog/item/Hexagon_SDK?version=6.4.0.2
+
+Unzip/untar the archive into
+```
+c:\Qualcomm\Hexagon_SDK\6.4.0.2
+```
+
+## Install the latest Adreno GPU driver
+
+Download the driver from
+
+    https://softwarecenter.qualcomm.com/catalog/item/Windows_Graphics_Driver
+
+After the automated installation and reboot please make sure that the GPU device shows up in the `Device Manager` (under 'Display Adapters`)
+
+## Install the latest Qualcomm NPU driver
+
+Download the driver from
+
+    https://softwarecenter.qualcomm.com/catalog/item/Qualcomm_HND
+
+After the automated installation and reboot please make sure that the Hexagon NPU device shows up in the `Device Manager` (under `Neural Processors`).
+
+If the device is not available you can try installing all components (`qcnspmcdm8380`, `qcnspmcdm8380_ext`) manually.
+The components are extracted into
+```
+c:\QCDrivers\qcnspmcdm...
+```
+
+## Enable NPU driver test signatures
+
+Please note that the following steps are required only for the Hexagon NPU.
+Adreno GPU backend does not require test signatures.
+
+### Enable testsigning
+
+Use `bcdedit` to enable test-signing
+```
+> bcdedit /set TESTSIGNING ON
+```
+(Secure Boot may need to be disabled for this to work)
+
+Make sure test-signing is enabled after reboot
+```
+> bcdedit /enum
+...
+testsigning             Yes
+...
+```
+For additional details see Microsoft guide at
+
+   https://learn.microsoft.com/en-us/windows-hardware/drivers/install/the-testsigning-boot-configuration-option
+
+### Create personal certificate
+
+The tools required for this procedure are available as part of Windows SDK and Windows Driver Kit which should be
+installed as part of the MS Visual Studio.
+They are typically located at
+```
+c:\Program Files (x86)\Windows Kits\10\bin\10.0.26100.0
+```
+(replace 10.0.26100.0 with correct version).
+
+To create personal self-signed certificate run the following commands (either from cmd or power-shell):
+```
+> cd c:\Users\MyUser
+> mkdir Certs
+> cd Certs
+> makecert -r -pe -ss PrivateCertStore -n CN=GGML.HTP.v1 -eku 1.3.6.1.5.5.7.3.3 -sv ggml-htp-v1.pvk ggml-htp-v1.cer
+> pvk2pfx.exe -pvk ggml-htp-v1.pvk -spc ggml-htp-v1.cer -pfx ggml-htp-v1.pfx
+```
+(replace `MyUser` with your username).
+
+Add this certificate to `Trusted Root Certification Authorities` and `Trusted Publishers` stores.
+This can be done using `certlm` Certificate Manager tool.
+Right click on the certificate store, select `All Tasks -> Import` and follow the prompts to import the certificate from the
+PFX file you created above.
+
+For additional details see Microsoft guide at
+
+    https://learn.microsoft.com/en-us/windows-hardware/drivers/install/introduction-to-test-signing
+
+Make sure to save the PFX file, you will need it for the build procedures.
+Please note that the same certificate can be used for signing any number of builds.
+
+## Build Hexagon backend with signed HTP ops libraries
+
+The overall Hexagon backend build procedure for Windows on Snapdragon is the same as for other platforms.
+However, additional settings are required for generating and signing HTP Ops libraries.
+```
+> $env:OPENCL_SDK_ROOT="C:\Qualcomm\OpenCL_SDK\2.3.2"
+> $env:HEXAGON_SDK_ROOT="C:\Qualcomm\Hexagon_SDK\6.4.0.2"
+> $env:HEXAGON_TOOLS_ROOT="C:\Qualcomm\Hexagon_SDK\6.4.0.2\tools\HEXAGON_Tools\19.0.04"
+> $env:HEXAGON_HTP_CERT="c:\Users\MyUsers\Certs\ggml-htp-v1.pfx"
+> $env:WINDOWS_SDK_BIN="C:\Program Files (x86)\Windows Kits\10\bin\10.0.26100.0\arm64"
+
+> cmake --preset arm64-windows-snapdragon-release -B build-wos
+...
+> cmake --install build-wos --prefix pkg-snapdragon
+```
+
+Once the build is complete HTP ops libraries will be installed like this
+```
+> dir pkg-snapdragon/lib
+...
+-a----         1/22/2026   6:01 PM         187656 libggml-htp-v73.so
+-a----         1/22/2026   6:01 PM         191752 libggml-htp-v75.so
+-a----         1/22/2026   6:01 PM         187656 libggml-htp-v79.so
+-a----         1/22/2026   6:01 PM         187656 libggml-htp-v81.so
+-a----         1/22/2026   6:01 PM           4139 libggml-htp.cat
+```
+
+The .cat file, the signature and proper certicate installation can be verified with
+
+```
+> signtool.exe verify /v /pa .\pkg-snapdragon\lib\libggml-htp.cat
+Verifying: .\pkg-snapdragon\lib\libggml-htp.cat
+
+Signature Index: 0 (Primary Signature)
+Hash of file (sha256): 9820C664DA59D5EAE31DBB664127FCDAEF59CDC31502496BC567544EC2F401CF
+
+Signing Certificate Chain:
+        Issued to: GGML.HTP.v1
+...
+Successfully verified: .\pkg-snapdragon\lib\libggml-htp.cat
+...
+```

docs/build.md

@@ -252,9 +252,7 @@ CUDA_VISIBLE_DEVICES="-0" ./build/bin/llama-server --model /srv/models/llama.ggu
 
 The environment variable [`CUDA_SCALE_LAUNCH_QUEUES`](https://docs.nvidia.com/cuda/cuda-programming-guide/05-appendices/environment-variables.html#cuda-scale-launch-queues) controls the size of CUDA's command buffer, which determines how many GPU operations can be queued before the CPU must wait for the GPU to catch up. A larger buffer reduces CPU-side stalls and allows more work to be queued on a GPU.
 
-**Default behavior:** llama.cpp automatically sets `CUDA_SCALE_LAUNCH_QUEUES=4x`, which increases the CUDA command buffer to 4 times its default size. This optimization is particularly beneficial for **Multi-GPU setups with pipeline parallelism**, where it significantly improves prompt processing throughput by allowing more operations to be enqueued across GPUs.
-
-See PR [#19042](https://github.com/ggml-org/llama.cpp/pull/19042) for performance benchmarks and technical details.
+Consider setting `CUDA_SCALE_LAUNCH_QUEUES=4x`, which increases the CUDA command buffer to 4 times its default size. This optimization is particularly beneficial for **Multi-GPU setups with pipeline parallelism**, where it significantly improves prompt processing throughput by allowing more operations to be enqueued across GPUs.
 
 ### Unified Memory
 
@@ -495,6 +493,37 @@ Finally, after finishing your build, you should be able to do something like thi
 # ggml_vulkan: Using Intel(R) Graphics (ADL GT2) | uma: 1 | fp16: 1 | warp size: 32
 ```
 
+### For Mac users:
+
+Generally, follow LunarG's [Getting Started with the MacOS Vulkan SDK](https://vulkan.lunarg.com/doc/sdk/latest/mac/getting_started.html) guide for installation and setup of the Vulkan SDK. There are two options of Vulkan drivers on macOS, both of which implement translation layers to map Vulkan to Metal. They can be hot-swapped by setting the `VK_ICD_FILENAMES` environment variable to point to the respective ICD JSON file.
+
+Check the box for "KosmicKrisp" during the LunarG Vulkan SDK installation.
+
+Set environment variable for the LunarG Vulkan SDK after installation (and optionally add to your shell profile for persistence):
+```bash
+source /path/to/vulkan-sdk/setup-env.sh
+```
+
+#### Using MoltenVK
+
+MoltenVK is the default Vulkan driver installed with the LunarG Vulkan SDK on macOS, so you can use the above environment variable settings as is.
+
+#### Using KosmicKrisp
+
+Override the environment variable for KosmicKrisp:
+```bash
+export VK_ICD_FILENAMES=$VULKAN_SDK/share/vulkan/icd.d/libkosmickrisp_icd.json
+export VK_DRIVER_FILES=$VULKAN_SDK/share/vulkan/icd.d/libkosmickrisp_icd.json
+```
+
+#### Build
+
+This is the only step different from [above](#common-steps) instructions.
+```bash
+cmake -B build -DGGML_VULKAN=1 -DGGML_METAL=OFF
+cmake --build build --config Release
+```
+
 ## CANN
 This provides NPU acceleration using the AI cores of your Ascend NPU. And [CANN](https://www.hiascend.com/en/software/cann) is a hierarchical APIs to help you to quickly build AI applications and service based on Ascend NPU.
 

docs/multimodal/minicpmo2.6.md

@@ -9,7 +9,7 @@ Download [MiniCPM-o-2_6](https://huggingface.co/openbmb/MiniCPM-o-2_6) PyTorch m
 ### Build llama.cpp
 Readme modification time: 20250206
 
-If there are differences in usage, please refer to the official build [documentation](https://github.com/ggerganov/llama.cpp/blob/master/docs/build.md)
+If there are differences in usage, please refer to the official build [documentation](https://github.com/ggml-org/llama.cpp/blob/master/docs/build.md)
 
 Clone llama.cpp:
 ```bash

docs/multimodal/minicpmo4.0.md

@@ -8,11 +8,11 @@ Download [MiniCPM-o-4](https://huggingface.co/openbmb/MiniCPM-o-4) PyTorch model
 ### Build llama.cpp
 Readme modification time: 20250206
 
-If there are differences in usage, please refer to the official build [documentation](https://github.com/ggerganov/llama.cpp/blob/master/docs/build.md)
+If there are differences in usage, please refer to the official build [documentation](https://github.com/ggml-org/llama.cpp/blob/master/docs/build.md)
 
 Clone llama.cpp:
 ```bash
-git clone https://github.com/ggerganov/llama.cpp
+git clone https://github.com/ggml-org/llama.cpp
 cd llama.cpp
 ```
 

docs/multimodal/minicpmv2.5.md

@@ -8,7 +8,7 @@ Download [MiniCPM-Llama3-V-2_5](https://huggingface.co/openbmb/MiniCPM-Llama3-V-
 ### Build llama.cpp
 Readme modification time: 20250206
 
-If there are differences in usage, please refer to the official build [documentation](https://github.com/ggerganov/llama.cpp/blob/master/docs/build.md)
+If there are differences in usage, please refer to the official build [documentation](https://github.com/ggml-org/llama.cpp/blob/master/docs/build.md)
 
 Clone llama.cpp:
 ```bash

docs/multimodal/minicpmv2.6.md

@@ -8,7 +8,7 @@ Download [MiniCPM-V-2_6](https://huggingface.co/openbmb/MiniCPM-V-2_6) PyTorch m
 ### Build llama.cpp
 Readme modification time: 20250206
 
-If there are differences in usage, please refer to the official build [documentation](https://github.com/ggerganov/llama.cpp/blob/master/docs/build.md)
+If there are differences in usage, please refer to the official build [documentation](https://github.com/ggml-org/llama.cpp/blob/master/docs/build.md)
 
 Clone llama.cpp:
 ```bash

docs/multimodal/minicpmv4.0.md

@@ -8,11 +8,11 @@ Download [MiniCPM-V-4](https://huggingface.co/openbmb/MiniCPM-V-4) PyTorch model
 ### Build llama.cpp
 Readme modification time: 20250731
 
-If there are differences in usage, please refer to the official build [documentation](https://github.com/ggerganov/llama.cpp/blob/master/docs/build.md)
+If there are differences in usage, please refer to the official build [documentation](https://github.com/ggml-org/llama.cpp/blob/master/docs/build.md)
 
 Clone llama.cpp:
 ```bash
-git clone https://github.com/ggerganov/llama.cpp
+git clone https://github.com/ggml-org/llama.cpp
 cd llama.cpp
 ```
 

docs/multimodal/minicpmv4.5.md

@@ -8,11 +8,11 @@ Download [MiniCPM-V-4_5](https://huggingface.co/openbmb/MiniCPM-V-4_5) PyTorch m
 ### Build llama.cpp
 Readme modification time: 20250826
 
-If there are differences in usage, please refer to the official build [documentation](https://github.com/ggerganov/llama.cpp/blob/master/docs/build.md)
+If there are differences in usage, please refer to the official build [documentation](https://github.com/ggml-org/llama.cpp/blob/master/docs/build.md)
 
 Clone llama.cpp:
 ```bash
-git clone https://github.com/ggerganov/llama.cpp
+git clone https://github.com/ggml-org/llama.cpp
 cd llama.cpp
 ```
 

docs/ops.md

@@ -22,7 +22,7 @@ Legend:
 |                           ARANGE | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                           ARGMAX | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                          ARGSORT | ❌ | ✅ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ❌ | ❌ |
-|                             CEIL | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ✅ | ❌ | ❌ |
+|                             CEIL | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                            CLAMP | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                           CONCAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                             CONT | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ❌ | ❌ |
@@ -97,7 +97,7 @@ Legend:
 |                             SILU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                        SILU_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
 |                              SIN | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 | ❌ | ❌ | ❌ |
-|                         SOFTPLUS | ❌ | ❌ | ✅ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ✅ | ❌ | ❌ |
+|                         SOFTPLUS | ❌ | ❌ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                         SOFT_MAX | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                    SOFT_MAX_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ✅ | ❌ | ❌ | ❌ |
 |                        SOLVE_TRI | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ |
@@ -113,8 +113,8 @@ Legend:
 |                       SWIGLU_OAI | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                             TANH | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |               TIMESTEP_EMBEDDING | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                            TOP_K | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | 🟡 | ✅ | ❌ | ❌ |
-|                              TRI | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
+|                            TOP_K | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | 🟡 | 🟡 | ✅ | ❌ | ❌ |
+|                              TRI | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                            TRUNC | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ✅ | ❌ | ❌ |
 |                          UPSCALE | ❌ | 🟡 | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ | ❌ |
 |                            XIELU | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ |

docs/speculative.md

@@ -0,0 +1,183 @@
+# Speculative Decoding
+
+llama.cpp supports speculative decoding, a technique that can significantly accelerate token generation by predicting multiple tokens ahead of the main model.
+
+[Speculative decoding](https://en.wikipedia.org/wiki/Transformer_(deep_learning)#Speculative_decoding) leverages the fact that computing n tokens in a batch (as in prompt processing) is more efficient than computing n sequentially (as in response generation). By generating draft tokens quickly and then verifying them with the target model in a single batch, this approach can achieve substantial speedups when the draft predictions are frequently correct.
+
+## Implementations
+
+The `llama-server` application supports several implementations of speculative decoding. An implementation with draft model can be mixed with an implementation without draft model.
+
+### Draft Model (`draft`)
+
+A much smaller model (called the _draft model_) generates drafts.
+A draft model is the most used approach in speculative decoding.
+
+### n-gram Cache (`ngram-cache`)
+
+An n-gram is a sequence of n tokens. The n-gram cache implementation maintains statistics about short n-gram sequences.
+A draft is computed using probabilities derived from these statistics. External statistics can also be loaded from files for improved accuracy.
+
+See:
+
+- #5479, #6828, #6848
+
+### n-gram Map (`ngram-simple`, `ngram-map-*`)
+
+These implementations search the token history for patterns and use matching sequences as draft candidates.
+They require no additional model but rely on patterns that have already appeared in the generated text.
+An example to use this approach can be the rewriting of source code by a LLM.
+
+#### n-gram Map (`ngram-simple`)
+
+This implementation looks for the last n-gram in history that matches the current n-gram and creates a draft using the m tokens following the matched n-gram. It is the simplest self-speculative approach with minimal overhead.
+
+```
+llama-server [...] --spec-type ngram-simple --draft-max 64
+```
+
+#### n-gram Map Key (`ngram-map-k`)
+
+This implementation looks for the current n-gram of size n (called the _key_) in the token history. If the key n-gram is followed by the same m tokens (called the _mgram_) multiple times, it creates a draft using these m tokens. This approach requires a minimum number of occurrences (argument `--spec-ngram-min-hits`, default is 1) before generating drafts.
+
+The number of accepted tokens is stored for each used n-gram.
+
+**Example:**
+```
+llama-server [...] --spec-type ngram-map-k --draft-max 64
+```
+
+#### n-gram Map Key-4-Values (`ngram-map-k4v`)
+
+This experimental implementation looks for the current n-gram of size n (called the _key_) in the token history. For each key, up to four _values_ (n-grams of size m, called _mgrams_) are tracked. An internal statistic counts the occurrences of each mgram after the key n-gram. If one mgram is significantly more frequent than the others, it is used as the draft.
+
+The number of accepted tokens is stored for each used n-gram.
+
+**Example:** Server options to be used if there are a lot of longer repetitions.
+```
+llama-server [...] --spec-type ngram-map-k4v --spec-ngram-size-n 8 --spec-ngram-size-m 8 --spec-ngram-min-hits 2 --draft-max 64
+```
+
+### n-gram Mod (`ngram-mod`)
+
+Add basic ngram hasher for speculative decoding:
+
+- For each ngram, compute a hash using LCG
+- For each computed hash, store the next token
+- During speculation, iteratively compute the rolling hash of the last n tokens and pick the next token from the storage
+
+Some characteristics:
+
+- Lightweight (~16 MB)
+- Constant memory and complexity
+- Can generate variable draft lengths (i.e. m is not fixed)
+
+Currently, a single hash pool is shared across all server slots, so different requests can benefit from each other.
+
+**Sample usage:**
+
+```
+# notes:
+# - small `n` are not recommended
+# - MoEs require long drafts
+# - dense models: can reduce `--draft-min` and `--draft-max`
+
+llama-server ... --spec-type ngram-mod --spec-ngram-size-n 24 --draft-min 48 --draft-max 64
+```
+
+Applications:
+
+- Iterating over a block of text/code (e.g. in llama.vim)
+- Reasoning models (when they have to repeat their thinking in the final answer)
+- Summarization
+
+Example Video:
+
+- See #19164
+
+### Differences between ngram-simple, ngram-map and ngram-mod
+
+- ngram-simple looks for a previous matching n-gram and inserts the following m-gram.
+- ngram-map-k looks for a previous matching n-gram and inserts the following m-gram but uses an internal hash-map of n-grams in the current context window.
+- ngram-mod uses a hash pool which is shared across all server slots. The hash pool is a map from n-gram hash to the next token (not the next m-gram as in ngram-map).
+
+## Command-Line Options
+
+If a draft model is combined with a draftless decoding the draftless decoding has higher precedence.
+
+```
+--draft, --draft-n, --draft-max N       number of tokens to draft for speculative decoding (default: 16)
+                                        (env: LLAMA_ARG_DRAFT_MAX)
+--draft-min, --draft-n-min N            minimum number of draft tokens to use for speculative decoding
+                                        (default: 0)
+                                        (env: LLAMA_ARG_DRAFT_MIN)
+[...]
+--spec-type [none|ngram-cache|ngram-simple|ngram-map-k|ngram-map-k4v|ngram-mod]
+                                        type of speculative decoding to use when no draft model is provided
+                                        (default: none)
+--spec-ngram-size-n N                   ngram size N for ngram-simple/ngram-map speculative decoding, length
+                                        of lookup n-gram (default: 12)
+--spec-ngram-size-m N                   ngram size M for ngram-simple/ngram-map speculative decoding, length
+                                        of draft m-gram (default: 48)
+--spec-ngram-min-hits N                 minimum hits for ngram-map speculative decoding (default: 1)
+```
+
+### `--spec-type TYPE`
+
+Specifies a type of speculative decoding without draft model.
+
+| Type | Description |
+|------|-------------|
+| `none` | No speculative decoding (default) |
+| `ngram-cache` | Use n-gram cache lookup |
+| `ngram-simple` | Use simple n-gram pattern matching |
+| `ngram-map-k` | Use n-gram pattern matching with n-gram-keys |
+| `ngram-map-k4v` | Use n-gram pattern matching with n-gram-keys and up to four m-gram values (experimental) |
+| `ngram-mod` | Use basic ngram hasher for speculative decoding with shared pool |
+
+**Example:** Server-instance used to refactor source code.
+```bash
+./llama-server [...] --spec-type ngram-simple
+```
+
+### `--spec-ngram-size-n N`
+
+Sets the size N of the lookup n-gram for n-gram map based speculative decoding.
+The n-gram size N determines how many tokens in a row to look back when searching for matching patterns.
+
+### `--spec-ngram-size-m M`
+
+Sets the size M of the draft m-gram for n-gram map based speculative decoding.
+The m-gram size determines how many tokens to draft when a match is found.
+Larger values can provide more speedup but may reduce acceptance rate.
+
+### `--spec-ngram-min-hits H`
+
+This option defines how often a key has to appear in the token history to be used as a draft (default is 1).
+
+## Statistics
+Each speculative decoding implementation prints statistics.
+
+```
+draft acceptance rate = 0.57576 (  171 accepted /   297 generated)
+statistics ngram_simple: #calls = 15, #gen drafts = 5, #acc drafts = 5, #gen tokens = 187, #acc tokens = 73
+statistics draft: #calls = 10, #gen drafts = 10, #acc drafts = 10, #gen tokens = 110, #acc tokens = 98
+```
+
+```
+draft acceptance rate = 0.70312 (   90 accepted /   128 generated)
+statistics ngram_mod: #calls = 810, #gen drafts = 15, #acc drafts = 15, #gen tokens = 960, #acc tokens = 730, dur(b,g,a) = 0.149, 0.347, 0.005 ms
+```
+
+```
+statistics ngram_map_k: #calls(b,g,a) = 6 1690 26, #gen drafts = 26, #acc drafts = 26, #gen tokens = 1248, #acc tokens = 968, dur(b,g,a) = 2.234, 1.427, 0.016 ms
+```
+
+
+- `#calls(b,g,a)`: number of calls of begin (new prompt), generation and accumulation of this implementations
+- `#gen drafts`: number of drafts generated by this implementation
+- `#acc drafts`: number of drafts accepted (partially) by the main model
+- `#gen tokens`: number of tokens generated by this implementation (including rejected tokens)
+- `#acc tokens`: number of tokens accepted by the main model
+- `dur(b,g,a): durations of begin (new prompt), generation and accumulation (process acceptance).
+

examples/deprecation-warning/README.md

@@ -1,7 +1,7 @@
 # Migration notice for binary filenames
 
 > [!IMPORTANT]
-[2024 Jun 12] Binaries have been renamed w/ a `llama-` prefix. `main` is now `llama-cli`, `server` is `llama-server`, etc (https://github.com/ggerganov/llama.cpp/pull/7809)
+[2024 Jun 12] Binaries have been renamed w/ a `llama-` prefix. `main` is now `llama-cli`, `server` is `llama-server`, etc (https://github.com/ggml-org/llama.cpp/pull/7809)
 
 This migration was important, but it is a breaking change that may not always be immediately obvious to users.
 

examples/deprecation-warning/deprecation-warning.cpp

@@ -28,7 +28,7 @@ int main(int argc, char** argv) {
     fprintf(stdout, "\n");
     fprintf(stdout, "WARNING: The binary '%s' is deprecated.\n", filename.c_str());
     fprintf(stdout, " Please use '%s' instead.\n", replacement_filename.c_str());
-    fprintf(stdout, " See https://github.com/ggerganov/llama.cpp/tree/master/examples/deprecation-warning/README.md for more information.\n");
+    fprintf(stdout, " See https://github.com/ggml-org/llama.cpp/tree/master/examples/deprecation-warning/README.md for more information.\n");
     fprintf(stdout, "\n");
 
     return EXIT_FAILURE;

examples/json_schema_to_grammar.py

@@ -402,7 +402,7 @@ class SchemaConverter:
             Transforms a regular expression pattern into a GBNF rule.
 
             Input: https://json-schema.org/understanding-json-schema/reference/regular_expressions
-            Output: https://github.com/ggerganov/llama.cpp/blob/master/grammars/README.md
+            Output: https://github.com/ggml-org/llama.cpp/blob/master/grammars/README.md
 
             Unsupported features: negative/positive lookaheads, greedy/non-greedy modifiers.
 

examples/lookahead/lookahead.cpp

@@ -50,6 +50,12 @@ int main(int argc, char ** argv) {
     const int N = 5;  // n-gram size
     const int G = 15; // max verification n-grams
 
+    // lookahead requires W + G + 1 sequences for parallel Jacobi decoding
+    params.n_parallel = W + G + 1;
+
+    // unified KV cache is required for coupled sequences in batch splitting
+    params.kv_unified = true;
+
     // init llama.cpp
     llama_backend_init();
     llama_numa_init(params.numa);
@@ -115,7 +121,7 @@ int main(int argc, char ** argv) {
     // seq_id == 0           : the current input token
     // seq_id [1, W]         : tokens from the past N - 1 Jacobi iterations
     // seq_id [W + 1, W + G] : verification n-grams
-    llama_batch batch = llama_batch_init(params.n_ctx, 0, W + G + 1);
+    llama_batch batch = llama_batch_init(llama_n_ctx(ctx), 0, W + G + 1);
 
     // target model sampling context
     struct common_sampler * smpl = common_sampler_init(model, params.sampling);

examples/lookup/lookup-create.cpp

@@ -32,9 +32,9 @@ int main(int argc, char ** argv){
 
     common_ngram_cache ngram_cache;
     common_ngram_cache_update(ngram_cache, LLAMA_NGRAM_STATIC, LLAMA_NGRAM_STATIC, inp, inp.size(), true);
-    fprintf(stderr, "%s: hashing done, writing file to %s\n", __func__, params.lookup_cache_static.c_str());
+    fprintf(stderr, "%s: hashing done, writing file to %s\n", __func__, params.speculative.lookup_cache_static.c_str());
 
-    common_ngram_cache_save(ngram_cache, params.lookup_cache_static);
+    common_ngram_cache_save(ngram_cache, params.speculative.lookup_cache_static);
 
     return 0;
 }

examples/lookup/lookup-stats.cpp

@@ -46,18 +46,18 @@ int main(int argc, char ** argv){
     {
         const int64_t t_start_draft_us = ggml_time_us();
 
-        if (!params.lookup_cache_static.empty()) {
+        if (!params.speculative.lookup_cache_static.empty()) {
             try {
-                ngram_cache_static = common_ngram_cache_load(params.lookup_cache_static);
+                ngram_cache_static = common_ngram_cache_load(params.speculative.lookup_cache_static);
             } catch (std::ifstream::failure const &) {
-                LOG_ERR("failed to open static lookup cache: %s", params.lookup_cache_static.c_str());
+                LOG_ERR("failed to open static lookup cache: %s", params.speculative.lookup_cache_static.c_str());
                 exit(1);
             }
         }
 
-        if (!params.lookup_cache_dynamic.empty()) {
+        if (!params.speculative.lookup_cache_dynamic.empty()) {
             try {
-                ngram_cache_dynamic = common_ngram_cache_load(params.lookup_cache_dynamic);
+                ngram_cache_dynamic = common_ngram_cache_load(params.speculative.lookup_cache_dynamic);
             } catch (std::ifstream::failure const &) {} // if the file does not exist it will simply be created at the end of the program
         }
 

examples/lookup/lookup.cpp

@@ -51,18 +51,18 @@ int main(int argc, char ** argv){
         const int64_t t_start_draft_us = ggml_time_us();
         common_ngram_cache_update(ngram_cache_context, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, inp, inp.size(), false);
 
-        if (!params.lookup_cache_static.empty()) {
+        if (!params.speculative.lookup_cache_static.empty()) {
             try {
-                ngram_cache_static = common_ngram_cache_load(params.lookup_cache_static);
+                ngram_cache_static = common_ngram_cache_load(params.speculative.lookup_cache_static);
             } catch (std::ifstream::failure const &) {
-                LOG_ERR("failed to open static lookup cache: %s", params.lookup_cache_static.c_str());
+                LOG_ERR("failed to open static lookup cache: %s", params.speculative.lookup_cache_static.c_str());
                 exit(1);
             }
         }
 
-        if (!params.lookup_cache_dynamic.empty()) {
+        if (!params.speculative.lookup_cache_dynamic.empty()) {
             try {
-                ngram_cache_dynamic = common_ngram_cache_load(params.lookup_cache_dynamic);
+                ngram_cache_dynamic = common_ngram_cache_load(params.speculative.lookup_cache_dynamic);
             } catch (std::ifstream::failure const &) {} // if the file does not exist it will simply be created at the end of the program
         }
 
@@ -106,7 +106,7 @@ int main(int argc, char ** argv){
 
     std::vector<llama_token> draft;
 
-    llama_batch batch_tgt = llama_batch_init(params.n_ctx, 0, 1);
+    llama_batch batch_tgt = llama_batch_init(llama_n_ctx(ctx), 0, 1);
 
     const auto t_dec_start = ggml_time_us();
 
@@ -210,7 +210,7 @@ int main(int argc, char ** argv){
 
     // Update dynamic ngram cache with context ngram cache and save it to disk:
     common_ngram_cache_merge(ngram_cache_dynamic, ngram_cache_context);
-    common_ngram_cache_save(ngram_cache_dynamic, params.lookup_cache_dynamic);
+    common_ngram_cache_save(ngram_cache_dynamic, params.speculative.lookup_cache_dynamic);
 
     LOG("\n\n");
 

examples/model-conversion/Makefile

@@ -33,11 +33,14 @@ DEVICE ?= auto
 causal-convert-model-bf16: OUTTYPE=bf16
 causal-convert-model-bf16: causal-convert-model
 
+causal-convert-model-debug: DEBUG=--debug
+causal-convert-model-debug: causal-convert-model
+
 causal-convert-model:
 	$(call validate_model_path,causal-convert-model)
 	@MODEL_NAME="$(MODEL_NAME)" OUTTYPE="$(OUTTYPE)" MODEL_PATH="$(MODEL_PATH)" \
 	METADATA_OVERRIDE="$(METADATA_OVERRIDE)" \
-	./scripts/causal/convert-model.sh
+	./scripts/causal/convert-model.sh $(DEBUG)
 
 causal-convert-mm-model-bf16: OUTTYPE=bf16
 causal-convert-mm-model-bf16: MM_OUTTYPE=f16

examples/model-conversion/scripts/causal/convert-model.sh

@@ -4,12 +4,17 @@ set -e
 
 # Parse command line arguments
 MMPROJ=""
+DEBUG=""
 while [[ $# -gt 0 ]]; do
     case $1 in
         --mmproj)
             MMPROJ="--mmproj"
             shift
             ;;
+        --debug)
+            DEBUG="1"
+            shift
+            ;;
         *)
             shift
             ;;
@@ -28,7 +33,12 @@ echo "Data  type: ${TYPE}"
 echo "Converted model path:: ${CONVERTED_MODEL}"
 echo "Metadata override: ${METADATA_OVERRIDE}"
 
-CMD_ARGS=("python" "../../convert_hf_to_gguf.py" "--verbose")
+if [[ -n "$DEBUG" ]]; then
+    CMD_ARGS=("python" "-m" "pdb")
+else
+    CMD_ARGS=("python")
+fi
+CMD_ARGS+=("../../convert_hf_to_gguf.py" "--verbose")
 CMD_ARGS+=("${MODEL_PATH}")
 CMD_ARGS+=("--outfile" "${CONVERTED_MODEL}")
 CMD_ARGS+=("--outtype" "${TYPE}")

examples/model-conversion/scripts/utils/tensor-info.py

@@ -0,0 +1,159 @@
+#!/usr/bin/env python3
+
+import argparse
+import json
+import os
+import re
+import sys
+from pathlib import Path
+from typing import Optional
+from safetensors import safe_open
+
+
+MODEL_SAFETENSORS_FILE = "model.safetensors"
+MODEL_SAFETENSORS_INDEX = "model.safetensors.index.json"
+
+
+def get_weight_map(model_path: Path) -> Optional[dict[str, str]]:
+    index_file = model_path / MODEL_SAFETENSORS_INDEX
+
+    if index_file.exists():
+        with open(index_file, 'r') as f:
+            index = json.load(f)
+            return index.get("weight_map", {})
+
+    return None
+
+
+def get_all_tensor_names(model_path: Path) -> list[str]:
+    weight_map = get_weight_map(model_path)
+
+    if weight_map is not None:
+        return list(weight_map.keys())
+
+    single_file = model_path / MODEL_SAFETENSORS_FILE
+    if single_file.exists():
+        try:
+            with safe_open(single_file, framework="pt", device="cpu") as f:
+                return list(f.keys())
+        except Exception as e:
+            print(f"Error reading {single_file}: {e}")
+            sys.exit(1)
+
+    print(f"Error: No safetensors files found in {model_path}")
+    sys.exit(1)
+
+
+def find_tensor_file(model_path: Path, tensor_name: str) -> Optional[str]:
+    weight_map = get_weight_map(model_path)
+
+    if weight_map is not None:
+        return weight_map.get(tensor_name)
+
+    single_file = model_path / MODEL_SAFETENSORS_FILE
+    if single_file.exists():
+        return single_file.name
+
+    return None
+
+
+def normalize_tensor_name(tensor_name: str) -> str:
+    normalized = re.sub(r'\.\d+\.', '.#.', tensor_name)
+    normalized = re.sub(r'\.\d+$', '.#', normalized)
+    return normalized
+
+
+def list_all_tensors(model_path: Path, unique: bool = False):
+    tensor_names = get_all_tensor_names(model_path)
+
+    if unique:
+        seen = set()
+        for tensor_name in sorted(tensor_names):
+            normalized = normalize_tensor_name(tensor_name)
+            if normalized not in seen:
+                seen.add(normalized)
+                print(normalized)
+    else:
+        for tensor_name in sorted(tensor_names):
+            print(tensor_name)
+
+
+def print_tensor_info(model_path: Path, tensor_name: str):
+    tensor_file = find_tensor_file(model_path, tensor_name)
+
+    if tensor_file is None:
+        print(f"Error: Could not find tensor '{tensor_name}' in model index")
+        print(f"Model path: {model_path}")
+        sys.exit(1)
+
+    file_path = model_path / tensor_file
+
+    try:
+        with safe_open(file_path, framework="pt", device="cpu") as f:
+            if tensor_name in f.keys():
+                tensor_slice = f.get_slice(tensor_name)
+                shape = tensor_slice.get_shape()
+                print(f"Tensor: {tensor_name}")
+                print(f"File:   {tensor_file}")
+                print(f"Shape:  {shape}")
+            else:
+                print(f"Error: Tensor '{tensor_name}' not found in {tensor_file}")
+                sys.exit(1)
+
+    except FileNotFoundError:
+        print(f"Error: The file '{file_path}' was not found.")
+        sys.exit(1)
+    except Exception as e:
+        print(f"An error occurred: {e}")
+        sys.exit(1)
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Print tensor information from a safetensors model"
+    )
+    parser.add_argument(
+        "tensor_name",
+        nargs="?",  # optional (if --list is used for example)
+        help="Name of the tensor to inspect"
+    )
+    parser.add_argument(
+        "-m", "--model-path",
+        type=Path,
+        help="Path to the model directory (default: MODEL_PATH environment variable)"
+    )
+    parser.add_argument(
+        "-l", "--list",
+        action="store_true",
+        help="List unique tensor patterns in the model (layer numbers replaced with #)"
+    )
+
+    args = parser.parse_args()
+
+    model_path = args.model_path
+    if model_path is None:
+        model_path_str = os.environ.get("MODEL_PATH")
+        if model_path_str is None:
+            print("Error: --model-path not provided and MODEL_PATH environment variable not set")
+            sys.exit(1)
+        model_path = Path(model_path_str)
+
+    if not model_path.exists():
+        print(f"Error: Model path does not exist: {model_path}")
+        sys.exit(1)
+
+    if not model_path.is_dir():
+        print(f"Error: Model path is not a directory: {model_path}")
+        sys.exit(1)
+
+    if args.list:
+        list_all_tensors(model_path, unique=True)
+    else:
+        if args.tensor_name is None:
+            print("Error: tensor_name is required when not using --list")
+            sys.exit(1)
+        print_tensor_info(model_path, args.tensor_name)
+
+
+if __name__ == "__main__":
+    main()

examples/speculative-simple/speculative-simple.cpp

@@ -24,7 +24,7 @@ int main(int argc, char ** argv) {
 
     common_init();
 
-    if (params.speculative.model.path.empty()) {
+    if (params.speculative.mparams_dft.path.empty()) {
         LOG_ERR("%s: --model-draft is required\n", __func__);
         return 1;
     }
@@ -34,10 +34,8 @@ int main(int argc, char ** argv) {
     llama_numa_init(params.numa);
 
     llama_model * model_tgt = NULL;
-    //llama_model * model_dft = NULL;
 
     llama_context * ctx_tgt = NULL;
-    llama_context * ctx_dft = NULL;
 
     // load the target model
     auto llama_init_tgt = common_init_from_params(params);
@@ -48,26 +46,38 @@ int main(int argc, char ** argv) {
     const llama_vocab * vocab = llama_model_get_vocab(model_tgt);
 
     // load the draft model
-    params.devices      = params.speculative.devices;
-    params.model        = params.speculative.model;
-    params.n_ctx        = params.speculative.n_ctx;
-    params.n_batch      = params.speculative.n_ctx > 0 ? params.speculative.n_ctx : params.n_batch;
-    params.n_gpu_layers = params.speculative.n_gpu_layers;
+    llama_model_ptr model_dft;
 
-    if (params.speculative.cpuparams.n_threads > 0) {
-        params.cpuparams.n_threads = params.speculative.cpuparams.n_threads;
-    }
+    // TODO: simplify this logic
+    {
+        const auto & params_spec = params.speculative;
 
-    params.cpuparams_batch.n_threads = params.speculative.cpuparams_batch.n_threads;
-    params.tensor_buft_overrides     = params.speculative.tensor_buft_overrides;
+        auto params_dft = params;
 
-    auto llama_init_dft = common_init_from_params(params);
+        params_dft.n_parallel   = 1;
+        params_dft.n_ctx        = params_spec.n_ctx;
+        params_dft.n_batch      = llama_n_ctx_seq(ctx_tgt);
+        params_dft.devices      = params_spec.devices;
+        params_dft.model        = params_spec.mparams_dft;
+        params_dft.n_gpu_layers = params_spec.n_gpu_layers;
 
-    //model_dft = llama_init_dft->model();
-    ctx_dft   = llama_init_dft->context();
+        if (params_spec.cpuparams.n_threads > 0) {
+            params_dft.cpuparams.n_threads       = params.speculative.cpuparams.n_threads;
+            params_dft.cpuparams_batch.n_threads = params.speculative.cpuparams_batch.n_threads;
+        }
 
-    if (!common_speculative_are_compatible(ctx_tgt, ctx_dft)) {
-        LOG_INF("the draft model '%s' is not compatible with the target model '%s'. tokens will be translated between the draft and target models.\n", params.speculative.model.path.c_str(), params.model.path.c_str());
+        params_dft.tensor_buft_overrides = params.speculative.tensor_buft_overrides;
+
+        auto mparams_dft = common_model_params_to_llama(params_dft);
+
+        model_dft.reset(llama_model_load_from_file(params_dft.model.path.c_str(), mparams_dft));
+        if (model_dft == nullptr) {
+            LOG_ERR("failed to load draft model, '%s'\n", params_dft.model.path.c_str());
+            return 1;
+        }
+
+        params.speculative.model_dft = model_dft.get();
+        params.speculative.cparams_dft = common_context_params_to_llama(params_dft);
     }
 
     // Tokenize the prompt
@@ -92,12 +102,6 @@ int main(int argc, char ** argv) {
         LOG("%s", common_token_to_piece(ctx_tgt, id).c_str());
     }
 
-    // how many tokens to draft each time
-    int n_draft     = params.speculative.n_max;
-    int n_draft_min = params.speculative.n_min;
-
-    float p_min = params.speculative.p_min;
-
     int n_predict = 0;
     int n_drafted = 0;
     int n_accept  = 0;
@@ -127,15 +131,11 @@ int main(int argc, char ** argv) {
     int n_past = inp.size() - 1;
 
     // init the speculator
-    struct common_speculative_params params_spec;
-    params_spec.n_draft = n_draft;
-    params_spec.n_reuse = llama_n_ctx(ctx_dft) - n_draft;
-    params_spec.p_min   = p_min;
+    const auto & params_spec = params.speculative;
 
-    struct common_speculative * spec = common_speculative_init(ctx_tgt, ctx_dft);
-    for (auto &pair : params.speculative.replacements) {
-        common_speculative_add_replacement_tgt_dft(spec, pair.first.c_str(), pair.second.c_str());
-    }
+    struct common_speculative * spec = common_speculative_init(params.speculative, ctx_tgt);
+
+    common_speculative_begin(spec, prompt_tgt);
 
     llama_batch batch_tgt = llama_batch_init(llama_n_batch(ctx_tgt), 0, 1);
 
@@ -151,7 +151,7 @@ int main(int argc, char ** argv) {
         // offloaded to a remote device. it doesn't even have to be based on an LLM. instead, it can provide tokens
         // from a cache or lookup tables.
         //
-        llama_tokens draft = common_speculative_gen_draft(spec, params_spec, prompt_tgt, id_last);
+        llama_tokens draft = common_speculative_draft(spec, params_spec, prompt_tgt, id_last);
 
         //LOG_DBG("draft: %s\n", string_from(ctx_dft, draft).c_str());
 
@@ -162,7 +162,7 @@ int main(int argc, char ** argv) {
         // evaluate the target model on [id_last, draft0, draft1, ..., draftN-1]
         {
             // do not waste time on small drafts
-            if (draft.size() < (size_t) n_draft_min) {
+            if (draft.size() < (size_t) params_spec.n_min) {
                 draft.clear();
             }
 
@@ -240,7 +240,7 @@ int main(int argc, char ** argv) {
     LOG_INF("decoded %4d tokens in %8.3f seconds, speed: %8.3f t/s\n", n_predict, (t_dec_end - t_dec_start) / 1e6f, n_predict  / ((t_dec_end - t_dec_start) / 1e6f));
 
     LOG_INF("\n");
-    LOG_INF("n_draft   = %d\n", n_draft);
+    LOG_INF("n_draft   = %d\n", params_spec.n_max);
     LOG_INF("n_predict = %d\n", n_predict);
     LOG_INF("n_drafted = %d\n", n_drafted);
     LOG_INF("n_accept  = %d\n", n_accept);
@@ -249,8 +249,6 @@ int main(int argc, char ** argv) {
     LOG_INF("\n");
     LOG_INF("draft:\n\n");
 
-    llama_perf_context_print(ctx_dft);
-
     LOG_INF("\n");
     LOG_INF("target:\n\n");
     common_perf_print(ctx_tgt, smpl);

examples/speculative/speculative.cpp

@@ -46,7 +46,7 @@ int main(int argc, char ** argv) {
 
     common_init();
 
-    if (params.speculative.model.path.empty()) {
+    if (params.speculative.mparams_dft.path.empty()) {
         LOG_ERR("%s: --model-draft is required\n", __func__);
         return 1;
     }
@@ -78,7 +78,7 @@ int main(int argc, char ** argv) {
 
     // load the draft model
     params.devices = params.speculative.devices;
-    params.model = params.speculative.model;
+    params.model = params.speculative.mparams_dft;
     params.n_gpu_layers = params.speculative.n_gpu_layers;
     if (params.speculative.cpuparams.n_threads > 0) {
         params.cpuparams.n_threads = params.speculative.cpuparams.n_threads;

examples/sycl/run-llama2.sh

@@ -18,13 +18,14 @@ CONTEXT=4096
 #support malloc device memory more than 4GB.
 export UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS=1
 
+LOAD_MODE='--mmap'
 if [ $# -gt 0 ]; then
     GGML_SYCL_DEVICE=$1
     echo "use $GGML_SYCL_DEVICE as main GPU"
     #use signle GPU only
-    ZES_ENABLE_SYSMAN=1 ./build/bin/llama-completion -m ${MODEL_FILE} -no-cnv -p "${INPUT_PROMPT}" -n 400 -e -ngl ${NGL} -s 0 -c ${CONTEXT} -mg $GGML_SYCL_DEVICE -sm none
+    ZES_ENABLE_SYSMAN=1 ./build/bin/llama-completion -m ${MODEL_FILE} -no-cnv -p "${INPUT_PROMPT}" -n 400 -e -ngl ${NGL} -s 0 -c ${CONTEXT} -mg $GGML_SYCL_DEVICE -sm none ${LOAD_MODE}
 
 else
     #use multiple GPUs with same max compute units
-    ZES_ENABLE_SYSMAN=1 ./build/bin/llama-completion -m ${MODEL_FILE} -no-cnv -p "${INPUT_PROMPT}" -n 400 -e -ngl ${NGL} -s 0 -c ${CONTEXT}
+    ZES_ENABLE_SYSMAN=1 ./build/bin/llama-completion -m ${MODEL_FILE} -no-cnv -p "${INPUT_PROMPT}" -n 400 -e -ngl ${NGL} -s 0 -c ${CONTEXT} ${LOAD_MODE}
 fi

examples/sycl/run-llama3.sh

@@ -1,31 +0,0 @@
-#!/usr/bin/env bash
-
-#  MIT license
-#  Copyright (C) 2025 Intel Corporation
-#  SPDX-License-Identifier: MIT
-
-# If you want more control, DPC++ Allows selecting a specific device through the
-# following environment variable
-export ONEAPI_DEVICE_SELECTOR="level_zero:0"
-source /opt/intel/oneapi/setvars.sh
-
-#export GGML_SYCL_DEBUG=1
-
-#ZES_ENABLE_SYSMAN=1, Support to get free memory of GPU by sycl::aspect::ext_intel_free_memory. Recommended to use when --split-mode = layer.
-
-INPUT_PROMPT="Building a website can be done in 10 simple steps:\nStep 1:"
-MODEL_FILE=models/Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf
-NGL=99 # Layers offloaded to the GPU. If the device runs out of memory, reduce this value according to the model you are using.
-CONTEXT=4096
-
-#support malloc device memory more than 4GB.
-export UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS=1
-
-if [ $# -gt 0 ]; then
-    GGML_SYCL_DEVICE=$1
-    echo "Using $GGML_SYCL_DEVICE as the main GPU"
-    ZES_ENABLE_SYSMAN=1 ./build/bin/llama-completion -m ${MODEL_FILE} -no-cnv -p "${INPUT_PROMPT}" -n 400 -e -ngl ${NGL} -s 0 -c ${CONTEXT} -mg $GGML_SYCL_DEVICE -sm none
-else
-    #use multiple GPUs with same max compute units
-    ZES_ENABLE_SYSMAN=1 ./build/bin/llama-completion -m ${MODEL_FILE} -no-cnv -p "${INPUT_PROMPT}" -n 400 -e -ngl ${NGL} -s 0 -c ${CONTEXT}
-fi

examples/sycl/test.sh

@@ -0,0 +1,130 @@
+#!/bin/bash
+
+#  MIT license
+#  Copyright (C) 2024 Intel Corporation
+#  SPDX-License-Identifier: MIT
+
+Help() {
+  cat << EOF
+Usage: $(basename "$0") [OPTIONS]
+
+This script processes files with specified options.
+
+Options:
+  -h, --help    Display this help message and exit.
+  -c, --context <value>    Set context length. Bigger need more memory.
+  -p, --promote <value>    Prompt to start generation with.
+  -m, --model   <value>    Full model file path.
+  -mg,--main-gpu <value>   Set main GPU ID (0 - n) for single GPU mode.
+  -sm,--split-mode <value> How to split the model across multiple GPUs, one of:
+                            - none: use one GPU only
+                            - layer (default): split layers and KV across GPUs
+                            - row: split rows across GPUs
+  -ngl,--n-gpu-layers <value>  Max. number of layers to store in VRAM (default: -1)
+  -lv,--log-verbosity <value>  Set the verbosity threshold. Messages with a higher verbosity will be
+                               ignored. Values:
+                                - 0: generic output
+                                - 1: error
+                                - 2: warning
+                                - 3: info
+                                - 4: debug
+
+
+EOF
+}
+
+BIN_FILE=./build/bin/llama-completion
+SEED=0
+GPUS_SETTING=""
+
+INPUT_PROMPT="Building a website can be done in 10 simple steps:\nStep 1:"
+MODEL_FILE=models/llama-2-7b.Q4_0.gguf
+NGL=99
+CONTEXT=4096
+GGML_SYCL_DEVICE=-1
+SPLIT_MODE=layer
+LOG_VERBOSE=3
+while [[ $# -gt 0 ]]; do
+    case "$1" in
+        -c|--context)
+            CONTEXT=$2
+            # Shift twice to consume both the option flag and its value
+            shift
+            shift
+            ;;
+        -p|--promote)
+            # Option that is a simple flag (boolean)
+            INPUT_PROMPT="$2"
+            # Shift once to consume the option flag
+            shift
+            shift
+            ;;
+        -m|--model)
+            MODEL_FILE="$2"
+            # Shift twice to consume both the option flag and its value
+            shift
+            shift
+            ;;
+        -mg|--main-gpu)
+            GGML_SYCL_DEVICE=$2
+            SPLIT_MODE=none
+            # Shift twice to consume both the option flag and its value
+            shift
+            shift
+            ;;
+        -sm|--split-mode)
+            SPLIT_MODE=$2
+            # Shift twice to consume both the option flag and its value
+            shift
+            shift
+            ;;
+        -ngl|--n-gpu-layers)
+            NGL=$2
+            # Shift twice to consume both the option flag and its value
+            shift
+            shift
+            ;;
+        -lv|--log-verbosity)
+            LOG_VERBOSE=$2
+            # Shift twice to consume both the option flag and its value
+            shift
+            shift
+            ;;
+        -h|--help)
+            Help
+            exit 0
+            ;;
+        *)
+            # Handle unknown options or stop processing options
+            echo "Invalid option: $1"
+            # Optional: exit script or shift to treat remaining as positional args
+            exit 1
+            ;;
+    esac
+done
+
+
+
+source /opt/intel/oneapi/setvars.sh
+
+#export GGML_SYCL_DEBUG=1
+
+#ZES_ENABLE_SYSMAN=1, Support to get free memory of GPU by sycl::aspect::ext_intel_free_memory. Recommended to use when --split-mode = layer.
+
+#support malloc device memory more than 4GB.
+export UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS=1
+echo "UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS=${UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS}"
+
+if [ $GGML_SYCL_DEVICE -ne -1 ]; then
+    echo "Use $GGML_SYCL_DEVICE as main GPU"
+    #use signle GPU only
+    GPUS_SETTING="-mg $GGML_SYCL_DEVICE -sm ${SPLIT_MODE}"
+    export ONEAPI_DEVICE_SELECTOR="level_zero:${$GGML_SYCL_DEVICE}"
+    echo "ONEAPI_DEVICE_SELECTOR=${ONEAPI_DEVICE_SELECTOR}"
+else
+   echo "Use all Intel GPUs, including iGPU & dGPU"
+ fi
+
+echo "run cmd: ZES_ENABLE_SYSMAN=1 ${BIN_FILE} -m ${MODEL_FILE} -no-cnv -p "${INPUT_PROMPT}" -n 400 -e -ngl ${NGL} -s ${SEED} -c ${CONTEXT} ${GPUS_SETTING} -lv ${LOG_VERBOSE}  --mmap "
+ZES_ENABLE_SYSMAN=1 ${BIN_FILE} -m ${MODEL_FILE} -no-cnv -p "${INPUT_PROMPT}" -n 400 -e -ngl ${NGL} -s ${SEED} -c ${CONTEXT} ${GPUS_SETTING} -lv ${LOG_VERBOSE} --mmap
+

examples/sycl/win-run-llama2.bat

@@ -7,5 +7,5 @@ set INPUT2="Building a website can be done in 10 simple steps:\nStep 1:"
 
 :: support malloc device memory more than 4GB.
 set UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS=1
-
-.\build\bin\llama-completion.exe -m models\llama-2-7b.Q4_0.gguf -no-cnv -p %INPUT2% -n 400 -e -ngl 99 -s 0
+set LOAD_MODE="--mmap"
+.\build\bin\llama-completion.exe -m models\llama-2-7b.Q4_0.gguf -no-cnv -p %INPUT2% -n 400 -e -ngl 99 -s 0 %LOAD_MODE%

examples/sycl/win-test.bat

@@ -7,5 +7,5 @@ set INPUT2="Building a website can be done in 10 simple steps:\nStep 1:"
 
 :: support malloc device memory more than 4GB.
 set UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS=1
-
-.\build\bin\llama-completion.exe -m models\Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf -no-cnv -p %INPUT2% -n 400 -s 0 -e -ngl 99
+set LOAD_MODE="--mmap"
+.\build\bin\llama-completion.exe -m models\llama-2-7b.Q4_0.gguf -no-cnv -p %INPUT2% -n 400 -e -ngl 99 -s 0 %LOAD_MODE%

ggml/CMakeLists.txt

@@ -1,4 +1,4 @@
-cmake_minimum_required(VERSION 3.14) # for add_link_options and implicit target directories.
+cmake_minimum_required(VERSION 3.14...3.28) # for add_link_options and implicit target directories.
 project("ggml" C CXX ASM)
 
 ### GGML Version
@@ -7,6 +7,8 @@ set(GGML_VERSION_MINOR 9)
 set(GGML_VERSION_PATCH 5)
 set(GGML_VERSION_BASE "${GGML_VERSION_MAJOR}.${GGML_VERSION_MINOR}.${GGML_VERSION_PATCH}")
 
+list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/cmake/")
+
 find_program(GIT_EXE NAMES git git.exe NO_CMAKE_FIND_ROOT_PATH)
 if(GIT_EXE)
     # Get current git commit hash
@@ -203,12 +205,14 @@ option(GGML_CUDA_NO_VMM                     "ggml: do not try to use CUDA VMM"
 option(GGML_CUDA_FA                         "ggml: compile ggml FlashAttention CUDA kernels"  ON)
 option(GGML_CUDA_FA_ALL_QUANTS              "ggml: compile all quants for FlashAttention"     OFF)
 option(GGML_CUDA_GRAPHS                     "ggml: use CUDA graphs (llama.cpp only)"          ${GGML_CUDA_GRAPHS_DEFAULT})
+option(GGML_CUDA_NCCL                       "ggml: use NVIDIA Collective Comm. Library"       ON)
 set   (GGML_CUDA_COMPRESSION_MODE "size" CACHE STRING
                                             "ggml: cuda link binary compression mode; requires cuda 12.8+")
 set_property(CACHE GGML_CUDA_COMPRESSION_MODE PROPERTY STRINGS "none;speed;balance;size")
 
 option(GGML_HIP                             "ggml: use HIP"                                   OFF)
 option(GGML_HIP_GRAPHS                      "ggml: use HIP graph, experimental, slow"         OFF)
+option(GGML_HIP_RCCL                        "ggml: use ROCm Collective Comm. Library"         OFF)
 option(GGML_HIP_NO_VMM                      "ggml: do not try to use HIP VMM"                 ON)
 option(GGML_HIP_ROCWMMA_FATTN               "ggml: enable rocWMMA for FlashAttention"         OFF)
 option(GGML_HIP_MMQ_MFMA                    "ggml: enable MFMA MMA for CDNA in MMQ"           ON)

ggml/cmake/FindNCCL.cmake

@@ -0,0 +1,36 @@
+# cmake/FindNCCL.cmake
+
+# NVIDIA does not distribute CMake files with NCCl, therefore use this file to find it instead.
+
+find_path(NCCL_INCLUDE_DIR
+    NAMES nccl.h
+    HINTS ${NCCL_ROOT} $ENV{NCCL_ROOT} $ENV{CUDA_HOME} /usr/local/cuda
+    PATH_SUFFIXES include
+)
+
+find_library(NCCL_LIBRARY
+    NAMES nccl
+    HINTS ${NCCL_ROOT} $ENV{NCCL_ROOT} $ENV{CUDA_HOME} /usr/local/cuda
+    PATH_SUFFIXES lib lib64
+)
+
+include(FindPackageHandleStandardArgs)
+find_package_handle_standard_args(NCCL
+    DEFAULT_MSG
+    NCCL_LIBRARY NCCL_INCLUDE_DIR
+)
+
+if(NCCL_FOUND)
+    set(NCCL_LIBRARIES ${NCCL_LIBRARY})
+    set(NCCL_INCLUDE_DIRS ${NCCL_INCLUDE_DIR})
+
+    if(NOT TARGET NCCL::NCCL)
+        add_library(NCCL::NCCL UNKNOWN IMPORTED)
+        set_target_properties(NCCL::NCCL PROPERTIES
+            IMPORTED_LOCATION "${NCCL_LIBRARY}"
+            INTERFACE_INCLUDE_DIRECTORIES "${NCCL_INCLUDE_DIR}"
+        )
+    endif()
+endif()
+
+mark_as_advanced(NCCL_INCLUDE_DIR NCCL_LIBRARY)

ggml/include/ggml-backend.h

@@ -68,7 +68,7 @@ extern "C" {
     GGML_API void                           ggml_backend_buffer_reset         (ggml_backend_buffer_t buffer);
 
     // tensor copy between different backends
-    GGML_API void ggml_backend_tensor_copy(struct ggml_tensor * src, struct ggml_tensor * dst);
+    GGML_API void ggml_backend_tensor_copy(const struct ggml_tensor * src, struct ggml_tensor * dst);
 
     //
     // Backend (stream)
@@ -83,13 +83,17 @@ extern "C" {
     GGML_API size_t                     ggml_backend_get_alignment(ggml_backend_t backend);
     GGML_API size_t                     ggml_backend_get_max_size(ggml_backend_t backend);
 
-    GGML_API void ggml_backend_tensor_set_async(ggml_backend_t backend,       struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
-    GGML_API void ggml_backend_tensor_get_async(ggml_backend_t backend, const struct ggml_tensor * tensor,       void * data, size_t offset, size_t size);
+    GGML_API void ggml_backend_tensor_set_async   (ggml_backend_t backend,       struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
+    GGML_API void ggml_backend_tensor_get_async   (ggml_backend_t backend, const struct ggml_tensor * tensor,       void * data, size_t offset, size_t size);
+    GGML_API void ggml_backend_tensor_set_2d_async(ggml_backend_t backend,       struct ggml_tensor * tensor, const void * data, size_t offset, size_t size, size_t n_copies, size_t stride_tensor, size_t stride_data);
+    GGML_API void ggml_backend_tensor_get_2d_async(ggml_backend_t backend, const struct ggml_tensor * tensor,       void * data, size_t offset, size_t size, size_t n_copies, size_t stride_tensor, size_t stride_data);
 
     // "offset" refers to the offset in tensor->data for setting/getting data
-    GGML_API void ggml_backend_tensor_set(      struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
-    GGML_API void ggml_backend_tensor_get(const struct ggml_tensor * tensor,       void * data, size_t offset, size_t size);
-    GGML_API void ggml_backend_tensor_memset(   struct ggml_tensor * tensor,     uint8_t value, size_t offset, size_t size);
+    GGML_API void ggml_backend_tensor_set   (      struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
+    GGML_API void ggml_backend_tensor_get   (const struct ggml_tensor * tensor,       void * data, size_t offset, size_t size);
+    GGML_API void ggml_backend_tensor_set_2d(      struct ggml_tensor * tensor, const void * data, size_t offset, size_t size, size_t n_copies, size_t stride_tensor, size_t stride_data);
+    GGML_API void ggml_backend_tensor_get_2d(const struct ggml_tensor * tensor,       void * data, size_t offset, size_t size, size_t n_copies, size_t stride_tensor, size_t stride_data);
+    GGML_API void ggml_backend_tensor_memset(      struct ggml_tensor * tensor,     uint8_t value, size_t offset, size_t size);
 
     GGML_API void ggml_backend_synchronize(ggml_backend_t backend);
 
@@ -109,7 +113,7 @@ extern "C" {
     // the copy is performed after all the currently queued operations in backend_src
     // backend_dst will wait for the copy to complete before performing other operations
     // automatic fallback to sync copy if async is not supported
-    GGML_API void ggml_backend_tensor_copy_async(ggml_backend_t backend_src, ggml_backend_t backend_dst, struct ggml_tensor * src, struct ggml_tensor * dst);
+    GGML_API void ggml_backend_tensor_copy_async(ggml_backend_t backend_src, ggml_backend_t backend_dst, const struct ggml_tensor * src, struct ggml_tensor * dst);
 
     GGML_API ggml_backend_dev_t ggml_backend_get_device(ggml_backend_t backend);
 
@@ -135,7 +139,9 @@ extern "C" {
         // integrated GPU device using host memory
         GGML_BACKEND_DEVICE_TYPE_IGPU,
         // accelerator devices intended to be used together with the CPU backend (e.g. BLAS or AMX)
-        GGML_BACKEND_DEVICE_TYPE_ACCEL
+        GGML_BACKEND_DEVICE_TYPE_ACCEL,
+        // "meta" device wrapping multiple other devices for tensor parallelism
+        GGML_BACKEND_DEVICE_TYPE_META,
     };
 
     // functionality supported by the device
@@ -196,7 +202,9 @@ extern "C" {
 
     // Common functions that may be obtained using ggml_backend_reg_get_proc_address
 
-    // Split buffer type for tensor parallelism
+    // AllReduce operation for tensor parallelism (meta backend)
+    typedef bool                         (*ggml_backend_allreduce_tensor_t)(ggml_backend_t * backends, struct ggml_tensor ** tensors, size_t n_backends);
+    // Split buffer type for tensor parallelism (old)
     typedef ggml_backend_buffer_type_t   (*ggml_backend_split_buffer_type_t)(int main_device, const float * tensor_split);
     // Set the number of threads for the backend
     typedef void                         (*ggml_backend_set_n_threads_t)(ggml_backend_t backend, int n_threads);
@@ -211,6 +219,55 @@ extern "C" {
     };
     typedef struct ggml_backend_feature * (*ggml_backend_get_features_t)(ggml_backend_reg_t reg);
 
+    //
+    // Meta backend
+    //
+
+    enum ggml_backend_meta_split_state {
+        // tensor split by tensor dimensions:
+        GGML_BACKEND_SPLIT_STATE_BY_NE0   =  0,
+        GGML_BACKEND_SPLIT_STATE_BY_NE1   =  1,
+        GGML_BACKEND_SPLIT_STATE_BY_NE2   =  2,
+        GGML_BACKEND_SPLIT_STATE_BY_NE3   =  3,
+
+        GGML_BACKEND_SPLIT_STATE_MIRRORED = 10, // all values on all backends
+        GGML_BACKEND_SPLIT_STATE_PARTIAL  = 11, // each backend has a partial sum
+
+        // for internal bookkeeping only:
+        GGML_BACKEND_SPLIT_STATE_NONE     = 98,
+        GGML_BACKEND_SPLIT_STATE_UNKNOWN  = 99,
+    };
+
+    // function to assign split states for statically allocated tensors, compute tensor split states will be assigned to be compatible:
+    typedef enum ggml_backend_meta_split_state (*ggml_backend_meta_get_split_state_t)(const struct ggml_tensor * tensor, void * userdata);
+
+
+    GGML_API bool ggml_backend_dev_is_meta(ggml_backend_dev_t dev);
+    GGML_API size_t ggml_backend_meta_dev_n_devs(ggml_backend_dev_t meta_dev);
+    GGML_API ggml_backend_dev_t ggml_backend_meta_dev_simple_dev(ggml_backend_dev_t meta_dev, size_t index);
+
+    // create a new meta device from "simple" devices, meta buffer type/buffer/backend is then derived from this:
+    GGML_API ggml_backend_dev_t ggml_backend_meta_device(
+        ggml_backend_dev_t * devs, size_t n_devs, ggml_backend_meta_get_split_state_t get_split_state, void * get_split_state_ud);
+
+    GGML_API bool ggml_backend_buft_is_meta(ggml_backend_buffer_type_t buft);
+    GGML_API size_t ggml_backend_meta_buft_n_bufts(ggml_backend_buffer_type_t meta_buft);
+    GGML_API ggml_backend_buffer_type_t ggml_backend_meta_buft_simple_buft(ggml_backend_buffer_type_t meta_buft, size_t index);
+
+    GGML_API bool ggml_backend_buffer_is_meta(ggml_backend_buffer_t buf);
+    GGML_API size_t ggml_backend_meta_buffer_n_bufs(ggml_backend_buffer_t meta_buf);
+    GGML_API ggml_backend_buffer_t ggml_backend_meta_buffer_simple_buffer(ggml_backend_buffer_t meta_buf, size_t index);
+    GGML_API struct ggml_tensor * ggml_backend_meta_buffer_simple_tensor(const struct ggml_tensor * tensor, size_t index);
+
+    GGML_API bool ggml_backend_is_meta(ggml_backend_t backend);
+    GGML_API size_t ggml_backend_meta_n_backends(ggml_backend_t meta_backend);
+    GGML_API ggml_backend_t ggml_backend_meta_simple_backend(ggml_backend_t meta_backend, size_t index);
+
+    GGML_API enum ggml_backend_meta_split_state ggml_backend_meta_get_split_state(const struct ggml_tensor * tensor, bool assume_sync);
+
+    // temporary workaround to statically allocate tensors from a context in a deduplicated way:
+    GGML_API struct ggml_backend_buffer * ggml_backend_meta_alloc_ctx_tensors_from_buft(struct ggml_context * ctx, ggml_backend_buffer_type_t buft);
+
     //
     // Backend registry
     //

ggml/include/ggml-cann.h

@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2023-2024 The ggml authors
+ * Copyright (c) 2023-2026 The ggml authors
  *
  * Permission is hereby granted, free of charge, to any person obtaining a copy
  * of this software and associated documentation files (the "Software"), to

ggml/include/ggml-cpu.h

@@ -19,6 +19,9 @@ extern "C" {
         // abort ggml_graph_compute when true
         ggml_abort_callback abort_callback;
         void *              abort_callback_data;
+
+        // use only reference implementations
+        bool use_ref;
     };
 
     // numa strategies
@@ -132,6 +135,8 @@ extern "C" {
     GGML_BACKEND_API void ggml_backend_cpu_set_threadpool    (ggml_backend_t backend_cpu, ggml_threadpool_t threadpool);
     GGML_BACKEND_API void ggml_backend_cpu_set_abort_callback(ggml_backend_t backend_cpu, ggml_abort_callback abort_callback, void * abort_callback_data);
 
+    GGML_BACKEND_API void ggml_backend_cpu_set_use_ref(ggml_backend_t backend_cpu, bool use_ref);
+
     GGML_BACKEND_API ggml_backend_reg_t ggml_backend_cpu_reg(void);
 
     GGML_BACKEND_API void ggml_cpu_fp32_to_fp32(const float *,       float *, int64_t);

ggml/include/ggml-cuda.h

@@ -27,6 +27,9 @@ GGML_BACKEND_API bool ggml_backend_is_cuda(ggml_backend_t backend);
 // device buffer
 GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_cuda_buffer_type(int device);
 
+// conduct allreduce operation between devices
+GGML_BACKEND_API bool ggml_backend_cuda_allreduce_tensor(ggml_backend_t * backends, struct ggml_tensor ** tensors, size_t n_backends);
+
 // split tensor buffer that splits matrices by rows across multiple devices
 GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_cuda_split_buffer_type(int main_device, const float * tensor_split);
 

ggml/include/ggml-virtgpu.h

@@ -7,8 +7,6 @@
 extern "C" {
 #endif
 
-#define GGML_REMOTING_FRONTEND_NAME "RemotingFrontend"
-
 GGML_BACKEND_API ggml_backend_reg_t ggml_backend_virtgpu_reg();
 
 #ifdef  __cplusplus

ggml/include/ggml.h

@@ -6,7 +6,7 @@
 // This documentation is still a work in progress.
 // If you wish some specific topics to be covered, feel free to drop a comment:
 //
-//   https://github.com/ggerganov/whisper.cpp/issues/40
+//   https://github.com/ggml-org/whisper.cpp/issues/40
 //
 // ## Overview
 //

ggml/src/CMakeLists.txt

@@ -200,6 +200,7 @@ add_library(ggml-base
             ggml.cpp
             ggml-alloc.c
             ggml-backend.cpp
+            ggml-backend-meta.cpp
             ggml-opt.cpp
             ggml-threading.cpp
             ggml-threading.h
@@ -222,6 +223,7 @@ if (GGML_SCHED_NO_REALLOC)
 endif()
 
 add_library(ggml
+            ggml-backend-dl.cpp
             ggml-backend-reg.cpp)
 add_library(ggml::ggml ALIAS ggml)
 

ggml/src/ggml-backend-dl.cpp

@@ -0,0 +1,48 @@
+#include "ggml-backend-dl.h"
+
+#ifdef _WIN32
+
+dl_handle * dl_load_library(const fs::path & path) {
+    // suppress error dialogs for missing DLLs
+    DWORD old_mode = SetErrorMode(SEM_FAILCRITICALERRORS);
+    SetErrorMode(old_mode | SEM_FAILCRITICALERRORS);
+
+    HMODULE handle = LoadLibraryW(path.wstring().c_str());
+
+    SetErrorMode(old_mode);
+
+    return handle;
+}
+
+void * dl_get_sym(dl_handle * handle, const char * name) {
+    DWORD old_mode = SetErrorMode(SEM_FAILCRITICALERRORS);
+    SetErrorMode(old_mode | SEM_FAILCRITICALERRORS);
+
+    void * p = (void *) GetProcAddress(handle, name);
+
+    SetErrorMode(old_mode);
+
+    return p;
+}
+
+const char * dl_error() {
+    return "";
+}
+
+#else
+
+dl_handle * dl_load_library(const fs::path & path) {
+    dl_handle * handle = dlopen(path.string().c_str(), RTLD_NOW | RTLD_LOCAL);
+    return handle;
+}
+
+void * dl_get_sym(dl_handle * handle, const char * name) {
+    return dlsym(handle, name);
+}
+
+const char * dl_error() {
+    const char *rslt = dlerror();
+    return rslt != nullptr ? rslt : "";
+}
+
+#endif

ggml/src/ggml-backend-dl.h

@@ -0,0 +1,45 @@
+#pragma once
+
+#ifdef _WIN32
+#   define WIN32_LEAN_AND_MEAN
+#   ifndef NOMINMAX
+#       define NOMINMAX
+#   endif
+#   include <windows.h>
+#   include <winevt.h>
+#else
+#    include <dlfcn.h>
+#    include <unistd.h>
+#endif
+#include <filesystem>
+
+namespace fs = std::filesystem;
+
+#ifdef _WIN32
+
+using dl_handle = std::remove_pointer_t<HMODULE>;
+
+struct dl_handle_deleter {
+    void operator()(HMODULE handle) {
+        FreeLibrary(handle);
+    }
+};
+
+#else
+
+using dl_handle = void;
+
+struct dl_handle_deleter {
+    void operator()(void * handle) {
+        dlclose(handle);
+    }
+};
+
+#endif
+
+using dl_handle_ptr = std::unique_ptr<dl_handle, dl_handle_deleter>;
+
+dl_handle * dl_load_library(const fs::path & path);
+void * dl_get_sym(dl_handle * handle, const char * name);
+const char * dl_error();
+

ggml/src/ggml-backend-impl.h

@@ -2,7 +2,9 @@
 
 // ggml-backend internal header
 
+#include "ggml-alloc.h"
 #include "ggml-backend.h"
+#include "ggml.h"
 
 #ifdef  __cplusplus
 extern "C" {
@@ -49,6 +51,10 @@ extern "C" {
         void         (*memset_tensor)(ggml_backend_buffer_t buffer,       struct ggml_tensor * tensor,     uint8_t value, size_t offset, size_t size);
         void         (*set_tensor)   (ggml_backend_buffer_t buffer,       struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
         void         (*get_tensor)   (ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor,       void * data, size_t offset, size_t size);
+        // (optional) 2d data copies
+        void         (*set_tensor_2d)(ggml_backend_buffer_t buffer,       struct ggml_tensor * tensor, const void * data, size_t offset, size_t size, size_t n_copies, size_t stride_tensor, size_t stride_data);
+        void         (*get_tensor_2d)(ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor,       void * data, size_t offset, size_t size, size_t n_copies, size_t stride_tensor, size_t stride_data);
+
         // (optional) tensor copy: dst is in the buffer, src may be in any buffer, including buffers from a different backend (return false if not supported)
         bool         (*cpy_tensor)   (ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst);
         // clear the entire buffer
@@ -90,8 +96,10 @@ extern "C" {
         void (*free)(ggml_backend_t backend);
 
         // (optional) asynchronous tensor data access
-        void (*set_tensor_async)(ggml_backend_t backend,       struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
-        void (*get_tensor_async)(ggml_backend_t backend, const struct ggml_tensor * tensor,       void * data, size_t offset, size_t size);
+        void (*set_tensor_async)   (ggml_backend_t backend,       struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
+        void (*get_tensor_async)   (ggml_backend_t backend, const struct ggml_tensor * tensor,       void * data, size_t offset, size_t size);
+        void (*set_tensor_2d_async)(ggml_backend_t backend,       struct ggml_tensor * tensor, const void * data, size_t offset, size_t size, size_t n_copies, size_t stride_tensor, size_t stride_data);
+        void (*get_tensor_2d_async)(ggml_backend_t backend, const struct ggml_tensor * tensor,       void * data, size_t offset, size_t size, size_t n_copies, size_t stride_tensor, size_t stride_data);
         bool (*cpy_tensor_async)(ggml_backend_t backend_src, ggml_backend_t backend_dst, const struct ggml_tensor * src, struct ggml_tensor * dst);
 
         // (optional) complete all pending operations (required if the backend supports async operations)

ggml/src/ggml-backend-reg.cpp

@@ -1,5 +1,6 @@
 #include "ggml-backend-impl.h"
 #include "ggml-backend.h"
+#include "ggml-backend-dl.h"
 #include "ggml-impl.h"
 #include <algorithm>
 #include <cstring>
@@ -98,72 +99,6 @@ static std::string path_str(const fs::path & path) {
     }
 }
 
-#ifdef _WIN32
-
-using dl_handle = std::remove_pointer_t<HMODULE>;
-
-struct dl_handle_deleter {
-    void operator()(HMODULE handle) {
-        FreeLibrary(handle);
-    }
-};
-
-static dl_handle * dl_load_library(const fs::path & path) {
-    // suppress error dialogs for missing DLLs
-    DWORD old_mode = SetErrorMode(SEM_FAILCRITICALERRORS);
-    SetErrorMode(old_mode | SEM_FAILCRITICALERRORS);
-
-    HMODULE handle = LoadLibraryW(path.wstring().c_str());
-
-    SetErrorMode(old_mode);
-
-    return handle;
-}
-
-static void * dl_get_sym(dl_handle * handle, const char * name) {
-    DWORD old_mode = SetErrorMode(SEM_FAILCRITICALERRORS);
-    SetErrorMode(old_mode | SEM_FAILCRITICALERRORS);
-
-    void * p = (void *) GetProcAddress(handle, name);
-
-    SetErrorMode(old_mode);
-
-    return p;
-}
-
-static const char * dl_error() {
-    return "";
-}
-
-#else
-
-using dl_handle = void;
-
-struct dl_handle_deleter {
-    void operator()(void * handle) {
-        dlclose(handle);
-    }
-};
-
-static void * dl_load_library(const fs::path & path) {
-    dl_handle * handle = dlopen(path.string().c_str(), RTLD_NOW | RTLD_LOCAL);
-
-    return handle;
-}
-
-static void * dl_get_sym(dl_handle * handle, const char * name) {
-    return dlsym(handle, name);
-}
-
-static const char * dl_error() {
-    const char *rslt = dlerror();
-    return rslt != nullptr ? rslt : "";
-}
-
-#endif
-
-using dl_handle_ptr = std::unique_ptr<dl_handle, dl_handle_deleter>;
-
 struct ggml_backend_reg_entry {
     ggml_backend_reg_t reg;
     dl_handle_ptr handle;
@@ -536,9 +471,10 @@ static ggml_backend_reg_t ggml_backend_load_best(const char * name, bool silent,
 
     int best_score = 0;
     fs::path best_path;
+    std::error_code ec;
 
     for (const auto & search_path : search_paths) {
-        if (std::error_code ec; !fs::exists(search_path, ec)) {
+        if (!fs::exists(search_path, ec)) {
             if (ec) {
                 GGML_LOG_DEBUG("%s: posix_stat(%s) failure, error-message: %s\n", __func__, path_str(search_path).c_str(), ec.message().c_str());
             } else {
@@ -548,7 +484,7 @@ static ggml_backend_reg_t ggml_backend_load_best(const char * name, bool silent,
         }
         fs::directory_iterator dir_it(search_path, fs::directory_options::skip_permission_denied);
         for (const auto & entry : dir_it) {
-            if (entry.is_regular_file()) {
+            if (entry.is_regular_file(ec)) {
                 auto filename = entry.path().filename();
                 auto ext = entry.path().extension();
                 if (filename.native().find(file_prefix) == 0 && ext == file_extension) {

ggml/src/ggml-backend.cpp

@@ -123,7 +123,7 @@ size_t ggml_backend_buffer_get_size(ggml_backend_buffer_t buffer) {
 void * ggml_backend_buffer_get_base(ggml_backend_buffer_t buffer) {
     GGML_ASSERT(buffer);
     // get_base is optional if the buffer is zero-sized
-    if (buffer->size == 0) {
+    if (!ggml_backend_buffer_is_meta(buffer) && buffer->size == 0) {
         return NULL;
     }
 
@@ -258,6 +258,7 @@ void ggml_backend_tensor_set_async(ggml_backend_t backend, struct ggml_tensor *
     GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor write out of bounds");
 
     if (backend->iface.set_tensor_async == NULL) {
+        ggml_backend_synchronize(backend);
         ggml_backend_tensor_set(tensor, data, offset, size);
     } else {
         backend->iface.set_tensor_async(backend, tensor, data, offset, size);
@@ -271,21 +272,64 @@ void ggml_backend_tensor_get_async(ggml_backend_t backend, const struct ggml_ten
     GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor read out of bounds");
 
     if (backend->iface.get_tensor_async == NULL) {
+        ggml_backend_synchronize(backend);
         ggml_backend_tensor_get(tensor, data, offset, size);
     } else {
         backend->iface.get_tensor_async(backend, tensor, data, offset, size);
     }
 }
 
+void ggml_backend_tensor_set_2d_async(ggml_backend_t backend, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size,
+            size_t n_copies, size_t stride_tensor, size_t stride_data) {
+    GGML_ASSERT(backend);
+    GGML_ASSERT(tensor);
+    GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
+
+    if (n_copies <= 1 || backend->iface.set_tensor_2d_async == NULL) {
+        for (size_t i = 0; i < n_copies; i++) {
+            ggml_backend_tensor_set_async(backend, tensor, (const char *) data + i*stride_data, offset + i*stride_tensor, size);
+        }
+        return;
+    }
+    if (size == 0) {
+        return;
+    }
+
+    GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
+    GGML_ASSERT(offset + (n_copies-1)*stride_tensor + size <= ggml_nbytes(tensor) && "tensor write out of bounds");
+    backend->iface.set_tensor_2d_async(backend, tensor, data, offset, size, n_copies, stride_tensor, stride_data);
+}
+
+void ggml_backend_tensor_get_2d_async(ggml_backend_t backend, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size,
+            size_t n_copies, size_t stride_tensor, size_t stride_data) {
+    GGML_ASSERT(backend);
+    GGML_ASSERT(tensor);
+    GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
+
+    if (n_copies <= 1 || backend->iface.set_tensor_2d_async == NULL) {
+        for (size_t i = 0; i < n_copies; i++) {
+            ggml_backend_tensor_get_async(backend, tensor, (char *) data + i*stride_data, offset + i*stride_tensor, size);
+        }
+        return;
+    }
+    if (size == 0) {
+        return;
+    }
+
+    GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
+    GGML_ASSERT(offset + (n_copies-1)*stride_tensor + size <= ggml_nbytes(tensor) && "tensor write out of bounds");
+    backend->iface.get_tensor_2d_async(backend, tensor, data, offset, size, n_copies, stride_tensor, stride_data);
+}
+
 void ggml_backend_tensor_set(struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
     GGML_ASSERT(tensor);
     ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
+    GGML_ASSERT(buf != NULL && "tensor buffer not set");
 
     if (size == 0) {
         return;
     }
 
-    GGML_ASSERT(buf != NULL && "tensor buffer not set");
     GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
     GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor write out of bounds");
 
@@ -295,18 +339,62 @@ void ggml_backend_tensor_set(struct ggml_tensor * tensor, const void * data, siz
 void ggml_backend_tensor_get(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) {
     GGML_ASSERT(tensor);
     ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
+    GGML_ASSERT(buf != NULL && "tensor buffer not set");
 
     if (size == 0) {
         return;
     }
 
-    GGML_ASSERT(buf != NULL && "tensor buffer not set");
     GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
     GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor read out of bounds");
 
     buf->iface.get_tensor(buf, tensor, data, offset, size);
 }
 
+void ggml_backend_tensor_set_2d(struct ggml_tensor * tensor, const void * data, size_t offset, size_t size,
+            size_t n_copies, size_t stride_tensor, size_t stride_data) {
+    GGML_ASSERT(tensor);
+    ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
+    GGML_ASSERT(buf != NULL && "tensor buffer not set");
+
+    if (n_copies <= 1 || buf->iface.set_tensor_2d == NULL) {
+        for (size_t i = 0; i < n_copies; i++) {
+            ggml_backend_tensor_set(tensor, (const char *) data + i*stride_data, offset + i*stride_tensor, size);
+        }
+        return;
+    }
+    if (size == 0) {
+        return;
+    }
+
+    GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
+    GGML_ASSERT(offset + (n_copies-1)*stride_tensor + size <= ggml_nbytes(tensor) && "tensor write out of bounds");
+
+    buf->iface.set_tensor_2d(buf, tensor, data, offset, size, n_copies, stride_tensor, stride_data);
+}
+
+void ggml_backend_tensor_get_2d(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size,
+            size_t n_copies, size_t stride_tensor, size_t stride_data) {
+    GGML_ASSERT(tensor);
+    ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
+    GGML_ASSERT(buf != NULL && "tensor buffer not set");
+
+    if (n_copies <= 1 || buf->iface.set_tensor_2d == NULL) {
+        for (size_t i = 0; i < n_copies; i++) {
+            ggml_backend_tensor_get(tensor, (char *) data + i*stride_data, offset + i*stride_tensor, size);
+        }
+        return;
+    }
+    if (size == 0) {
+        return;
+    }
+
+    GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
+    GGML_ASSERT(offset + (n_copies-1)*stride_tensor + size <= ggml_nbytes(tensor) && "tensor read out of bounds");
+
+    buf->iface.get_tensor_2d(buf, tensor, data, offset, size, n_copies, stride_tensor, stride_data);
+}
+
 void ggml_backend_tensor_memset(struct ggml_tensor * tensor, uint8_t value, size_t offset, size_t size) {
     GGML_ASSERT(tensor);
     ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
@@ -386,7 +474,7 @@ ggml_backend_dev_t ggml_backend_get_device(ggml_backend_t backend) {
 
 // backend copy
 
-void ggml_backend_tensor_copy(struct ggml_tensor * src, struct ggml_tensor * dst) {
+void ggml_backend_tensor_copy(const struct ggml_tensor * src, struct ggml_tensor * dst) {
     GGML_ASSERT(ggml_are_same_layout(src, dst) && "cannot copy tensors with different layouts");
 
     if (src == dst) {
@@ -400,7 +488,7 @@ void ggml_backend_tensor_copy(struct ggml_tensor * src, struct ggml_tensor * dst
     } else if (!ggml_backend_buffer_copy_tensor(src, dst)) {
 #ifndef NDEBUG
         GGML_LOG_DEBUG("%s: warning: slow copy from %s to %s\n", __func__, ggml_backend_buffer_name(src->buffer), ggml_backend_buffer_name(dst->buffer));
-#endif
+#endif // NDEBUG
         size_t nbytes = ggml_nbytes(src);
         void * data = malloc(nbytes);
         ggml_backend_tensor_get(src, data, 0, nbytes);
@@ -409,7 +497,7 @@ void ggml_backend_tensor_copy(struct ggml_tensor * src, struct ggml_tensor * dst
     }
 }
 
-void ggml_backend_tensor_copy_async(ggml_backend_t backend_src, ggml_backend_t backend_dst, struct ggml_tensor * src, struct ggml_tensor * dst) {
+void ggml_backend_tensor_copy_async(ggml_backend_t backend_src, ggml_backend_t backend_dst, const struct ggml_tensor * src, struct ggml_tensor * dst) {
     GGML_ASSERT(ggml_are_same_layout(src, dst) && "cannot copy tensors with different layouts");
 
     if (src == dst) {
@@ -498,6 +586,7 @@ enum ggml_backend_dev_type ggml_backend_dev_type(ggml_backend_dev_t device) {
 }
 
 void ggml_backend_dev_get_props(ggml_backend_dev_t device, struct ggml_backend_dev_props * props) {
+    GGML_ASSERT(device);
     memset(props, 0, sizeof(*props));
     device->iface.get_props(device, props);
 }
@@ -608,6 +697,8 @@ static const struct ggml_backend_buffer_i ggml_backend_multi_buffer_i = {
     /* .memset_tensor   = */ NULL,
     /* .set_tensor      = */ NULL,
     /* .get_tensor      = */ NULL,
+    /* .set_tensor_2d   = */ NULL,
+    /* .get_tensor_2d   = */ NULL,
     /* .cpy_tensor      = */ NULL,
     /* .clear           = */ ggml_backend_multi_buffer_clear,
     /* .reset           = */ NULL,
@@ -1897,8 +1988,9 @@ enum ggml_status ggml_backend_tensor_alloc(ggml_backend_buffer_t buffer, struct
     GGML_ASSERT(tensor->data == NULL);
     GGML_ASSERT(tensor->view_src == NULL);
     GGML_ASSERT(addr >= ggml_backend_buffer_get_base(buffer));
-    GGML_ASSERT((char *)addr + ggml_backend_buffer_get_alloc_size(buffer, tensor) <=
-                (char *)ggml_backend_buffer_get_base(buffer) + ggml_backend_buffer_get_size(buffer));
+    GGML_ASSERT(ggml_backend_buffer_is_meta(buffer) ||
+        (char *) addr + ggml_backend_buffer_get_alloc_size(buffer, tensor) <=
+        (char *) ggml_backend_buffer_get_base(buffer) + ggml_backend_buffer_get_size(buffer));
 
     tensor->buffer = buffer;
     tensor->data = addr;
@@ -2172,6 +2264,8 @@ static const struct ggml_backend_buffer_i ggml_backend_cpu_buffer_i = {
     /* .memset_tensor   = */ ggml_backend_cpu_buffer_memset_tensor,
     /* .set_tensor      = */ ggml_backend_cpu_buffer_set_tensor,
     /* .get_tensor      = */ ggml_backend_cpu_buffer_get_tensor,
+    /* .set_tensor_2d   = */ NULL,
+    /* .get_tensor_2d   = */ NULL,
     /* .cpy_tensor      = */ ggml_backend_cpu_buffer_cpy_tensor,
     /* .clear           = */ ggml_backend_cpu_buffer_clear,
     /* .reset           = */ NULL,
@@ -2184,6 +2278,8 @@ static const struct ggml_backend_buffer_i ggml_backend_cpu_buffer_from_ptr_i = {
     /* .memset_tensor   = */ ggml_backend_cpu_buffer_memset_tensor,
     /* .set_tensor      = */ ggml_backend_cpu_buffer_set_tensor,
     /* .get_tensor      = */ ggml_backend_cpu_buffer_get_tensor,
+    /* .set_tensor_2d   = */ NULL,
+    /* .get_tensor_2d   = */ NULL,
     /* .cpy_tensor      = */ ggml_backend_cpu_buffer_cpy_tensor,
     /* .clear           = */ ggml_backend_cpu_buffer_clear,
     /* .reset           = */ NULL,

ggml/src/ggml-blas/ggml-blas.cpp

@@ -260,6 +260,8 @@ static struct ggml_backend_i blas_backend_i = {
     /* .get_name                = */ ggml_backend_blas_get_name,
     /* .free                    = */ ggml_backend_blas_free,
     /* .set_tensor_async        = */ NULL,
+    /* .get_tensor_2d_async     = */ NULL,
+    /* .set_tensor_2d_async     = */ NULL,
     /* .get_tensor_async        = */ NULL,
     /* .cpy_tensor_async        = */ NULL,
     /* .synchronize             = */ NULL,

ggml/src/ggml-cann/acl_tensor.cpp

@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2023-2024 The ggml authors
+ * Copyright (c) 2023-2026 The ggml authors
  *
  * Permission is hereby granted, free of charge, to any person obtaining a copy
  * of this software and associated documentation files (the "Software"), to

ggml/src/ggml-cann/acl_tensor.h

@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2023-2024 The ggml authors
+ * Copyright (c) 2023-2026 The ggml authors
  *
  * Permission is hereby granted, free of charge, to any person obtaining a copy
  * of this software and associated documentation files (the "Software"), to

ggml/src/ggml-cann/aclnn_ops.cpp

@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2023-2024 The ggml authors
+ * Copyright (c) 2023-2026 The ggml authors
  *
  * Permission is hereby granted, free of charge, to any person obtaining a copy
  * of this software and associated documentation files (the "Software"), to
@@ -3286,130 +3286,223 @@ static void ggml_cann_mul_mat_id_fp(ggml_backend_cann_context & ctx, ggml_tensor
 }
 
 /**
- * @brief Performs expert-specific matrix multiplication (MoE) with
- * quantized precision using the CANN backend.
+ * @brief Performs quantized matrix multiplication for Mixture of Experts (MoE)
+ * models using the CANN backend.
  *
- * This function executes a matrix multiplication operation tailored for
- * Mixture of Experts (MoE) models, where the input tensor is multiplied
- * with expert-specific quantized weight matrices. It leverages the CANN
- * backend to perform efficient low-precision computations and stores the
- * quantized result in the destination tensor `dst`.
+ * This function implements MUL_MAT_ID operation for quantized weight matrices
+ * (Q4_0 and Q8_0 formats). It selects expert-specific weight matrices based on
+ * the provided expert indices, and computes matrix multiplication using CANN's
+ * WeightQuantBatchMatmulV2 operator.
  *
- * Quantization techniques reduce memory footprint and improve performance
- * by using lower-bit representations (e.g., int8) instead of floating-point.
- * This function is designed to work with such formats and may incorporate
- * optimizations like identity-based fast paths or routing masks for sparse
- * expert selection.
+ * The function performs the following steps:
+ * 1. Converts input/output tensors to F16 format if necessary
+ * 2. Uses IndexSelect to extract expert-specific weights and scales based on indices
+ * 3. Performs quantized matrix multiplication for each expert using WeightQuantBatchMatmulV2
+ * 4. Converts output back to the target type if needed
  *
- * @param ctx The context for executing CANN backend operations.
- * @param dst The destination tensor where the quantized MoE multiplication result
- * will be stored.
+ * Tensor shapes:
+ * - dst:  [M, K, N, 1] - output tensor
+ * - src0: [D, M, A, 1] - quantized weight matrices (Q4_0 or Q8_0)
+ * - src1: [D, B, N, 1] - input activations (B = K for per-expert input, or B = 1 for broadcast)
+ * - ids:  [K, N] - expert indices for routing
  *
- * @note This function assumes quantized data types and is designed for
- * MoE architectures with potential sparse expert routing.
+ * @param ctx The CANN backend context for operation execution.
+ * @param dst The destination tensor where the multiplication result will be stored.
+ *
+ * @note Only Q4_0 and Q8_0 quantization formats are supported.
+ * @note The function handles automatic type conversion to/from F16 as needed by the hardware.
  */
 static void ggml_cann_mul_mat_id_quant(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
-    // TODO: Use aclnnGroupedMatMul
-    //dst   [M, K, N, 1]
-    ggml_tensor * src0 = dst->src[0];  //src0	[D, M, A, 1]
-    ggml_tensor * src1 = dst->src[1];  //src1	[D, B, N, 1], B = K or B = 1
-    ggml_tensor * ids  = dst->src[2];  //ids	[K, N]
+    // dst:  [M, K, N, 1]
+    // src0: [D, M, A, 1] - quantized weights
+    // src1: [D, B, N, 1] - input activations, B = K or B = 1
+    // ids:  [K, N] - expert indices
+    ggml_tensor * src0 = dst->src[0];
+    ggml_tensor * src1 = dst->src[1];
+    ggml_tensor * ids  = dst->src[2];
 
-    GGML_TENSOR_BINARY_OP_LOCALS
+    GGML_ASSERT(src0->ne[3] == 1);
+    GGML_ASSERT(src1->ne[3] == 1);
+    GGML_ASSERT(dst->ne[3] == 1);
+    GGML_ASSERT(src1->ne[2] == ids->ne[1]);
 
-    // copy index from npu to cpu
-    int64_t n_as  = ne02;        // A
-    int64_t n_ids = ids->ne[0];  // K
+    const int64_t        n_batches        = ids->ne[1];
+    const int64_t        n_select_experts = ids->ne[0];
+    const enum ggml_type type             = src0->type;
 
-    std::vector<char> ids_host(ggml_nbytes(ids));
-    ACL_CHECK(aclrtMemcpyAsync(ids_host.data(), ggml_nbytes(ids), ids->data, ggml_nbytes(ids),
-                               ACL_MEMCPY_DEVICE_TO_HOST, ctx.stream()));
-    ACL_CHECK(aclrtSynchronizeStream(ctx.stream()));
+    const int32_t group_size = QK8_0;  // Both Q4_0 and Q8_0 use group size of 32
+    GGML_ASSERT(group_size == QK4_0);
 
-    char * src0_original = (char *) src0->data;
-    char * src1_original = (char *) src1->data;
-    char * dst_original  = (char *) dst->data;
+    // Calculate element size for quantized weights
+    const float weight_elem_size =
+        (type == GGML_TYPE_Q4_0) ? 0.5f :
+        (type == GGML_TYPE_Q8_0) ? 1.0f :
+                                   (GGML_ABORT("MUL_MAT_ID only supports Q4_0 and Q8_0"), 0.0f);
 
-    ggml_tensor src0_row = *src0;
-    ggml_tensor src1_row = *src1;
-    ggml_tensor dst_row  = *dst;
+    // Calculate scale offset in memory
+    const size_t weight_size     = src0->ne[0] * src0->ne[1] * src0->ne[2] * weight_elem_size;
+    const size_t scale_elem_size = sizeof(uint16_t);
+    char *       scale_data      = (char *) src0->data + weight_size;
 
-    const enum ggml_type type = dst->src[0]->type;
-    float                weight_elem_size;
-    if (type == GGML_TYPE_Q4_0) {
-        weight_elem_size = float(sizeof(uint8_t)) / 2;
-    } else if (type == GGML_TYPE_Q8_0) {
-        weight_elem_size = float(sizeof(uint8_t));
-    } else {
-        GGML_ABORT("MUL_MAT_ID only support quant type Q4_0 and Q8_0 ");
-    }
+    // Allocate buffers for selected expert weights and scales
+    const size_t         selected_weight_size = src0->ne[0] * src0->ne[1] * n_select_experts * weight_elem_size;
+    ggml_cann_pool_alloc selected_weight_alloc(ctx.pool(), selected_weight_size);
+    void *               selected_weight_buffer = selected_weight_alloc.get();
 
-    // src0_row [D, M, 1, 1] weight without permute
-    src0_row.ne[2]       = 1;
-    src0_row.ne[3]       = 1;
-    src0_row.nb[0]       = weight_elem_size;
-    src0_row.nb[1]       = weight_elem_size * ne00;
-    src0_row.nb[2]       = weight_elem_size * ne00;
-    src0_row.nb[3]       = weight_elem_size * ne00;
-    size_t weight_stride = ne00 * ne01 * weight_elem_size;
-    size_t weight_size   = weight_stride * ne02 * ne03;
+    const size_t selected_scale_size = (src0->ne[0] / group_size) * src0->ne[1] * n_select_experts * scale_elem_size;
+    ggml_cann_pool_alloc selected_scale_alloc(ctx.pool(), selected_scale_size);
+    void *               selected_scale_buffer = selected_scale_alloc.get();
 
-    // scale [D, M, 1, 1] -> scale && permute
-    size_t scale_elem_size = sizeof(uint16_t);
-    size_t scale_stride    = src0->ne[1] * src0->ne[0] / QK8_0 * scale_elem_size;
+    // Helper lambda to allocate and cast tensor to F16 if needed
+    constexpr size_t f16_elem_size      = sizeof(uint16_t);
+    auto             prepare_f16_buffer = [&](ggml_tensor * tensor, ggml_cann_pool_alloc & allocator,
+                                  bool need_cast = false) -> void * {
+        if (tensor->type == GGML_TYPE_F16) {
+            return tensor->data;
+        }
 
-    // src1_row [D, 1, 1, 1] -> input
-    src1_row.ne[1] = 1;
-    src1_row.ne[2] = 1;
-    src1_row.ne[3] = 1;
-    src1_row.nb[2] = nb11;
-    src1_row.nb[3] = nb11;
+        size_t total_size = f16_elem_size;
+        for (int i = 0; i < GGML_MAX_DIMS; i++) {
+            total_size *= tensor->ne[i];
+        }
+        void * buffer = allocator.alloc(total_size);
 
-    // dst_row [M, 1, 1, 1] -> out
-    dst_row.ne[1] = 1;
-    dst_row.ne[2] = 1;
-    dst_row.ne[3] = 1;
-    dst_row.nb[2] = nb1;
-    dst_row.nb[3] = nb1;
+        if (need_cast == false) {
+            return buffer;
+        }
 
-    //create weight for one row
-    ggml_cann_pool_alloc weight_allocator(ctx.pool());
-    void *               weight_buffer = weight_allocator.alloc(nb02);
-    for (int64_t iid1 = 0; iid1 < ids->ne[1]; iid1++) {
-        for (int64_t id = 0; id < n_ids; id++) {
-            // expert index
-            int32_t i02 = *(int32_t *) (ids_host.data() + iid1 * ids->nb[1] + id * ids->nb[0]);
-            GGML_ASSERT(i02 >= 0 && i02 < n_as);
+        int64_t ne[GGML_MAX_DIMS];
+        size_t  nb[GGML_MAX_DIMS] = { f16_elem_size };
+        for (int i = 0; i < GGML_MAX_DIMS; i++) {
+            ne[i] = tensor->ne[i];
+            if (i > 0) {
+                nb[i] = nb[i - 1] * ne[i - 1];
+            }
+        }
 
-            // If B = 1 (broadcast), always use 0; otherwise, use id.
-            int64_t i11 = (ne11 == 1 ? 0 : id);
-            int64_t i12 = iid1;
+        acl_tensor_ptr src_tensor = ggml_cann_create_tensor(tensor);
+        acl_tensor_ptr f16_tensor = ggml_cann_create_tensor(buffer, ACL_FLOAT16, f16_elem_size, ne, nb, GGML_MAX_DIMS);
+        aclnn_cast(ctx, src_tensor.get(), f16_tensor.get(), ACL_FLOAT16);
 
-            int64_t i1 = id;
-            int64_t i2 = i12;
+        return buffer;
+    };
 
-            void * src0_tmp_ptr  = src0_original + i02 * weight_stride;
-            void * scale_tmp_ptr = src0_original + weight_size + i02 * scale_stride;
-            void * src1_tmp_ptr  = src1_original + i11 * nb11 + i12 * nb12;
-            void * dst_tmp_ptr   = dst_original + i1 * nb1 + i2 * nb2;
+    // Prepare input and output buffers
+    ggml_cann_pool_alloc input_alloc(ctx.pool());
+    void *               input_buffer = prepare_f16_buffer(src1, input_alloc, true);
 
-            // mem cpy
-            ACL_CHECK(aclrtMemcpyAsync(weight_buffer, weight_stride, src0_tmp_ptr, weight_stride,
-                                       ACL_MEMCPY_DEVICE_TO_DEVICE, ctx.stream()));
-            void * scale_buffer = (char *) weight_buffer + weight_stride;
-            ACL_CHECK(aclrtMemcpyAsync(scale_buffer, scale_stride, scale_tmp_ptr, scale_stride,
-                                       ACL_MEMCPY_DEVICE_TO_DEVICE, ctx.stream()));
+    ggml_cann_pool_alloc output_alloc(ctx.pool());
+    void *               output_buffer = prepare_f16_buffer(dst, output_alloc, false);
 
-            src0_row.data  = weight_buffer;
-            src1_row.data  = src1_tmp_ptr;
-            dst_row.data   = dst_tmp_ptr;
-            dst_row.src[0] = &src0_row;
-            dst_row.src[1] = &src1_row;
+    // Process each batch
+    for (int64_t batch_idx = 0; batch_idx < n_batches; batch_idx++) {
+        // Create index tensor for current batch
+        const size_t   index_offset  = batch_idx * ids->nb[1];
+        acl_tensor_ptr batch_indices = ggml_cann_create_tensor(ids, ids->ne, ids->nb, 1, ACL_FORMAT_ND, index_offset);
 
-            ggml_cann_mul_mat(ctx, &dst_row);
+        // Select quantized weights using expert indices
+        // Q4_0 stores 2 values per byte, Q8_0 stores 1 value per byte
+        const int64_t weight_d         = (type == GGML_TYPE_Q4_0) ? src0->ne[0] / 2 : src0->ne[0];
+        const int64_t weight_m         = src0->ne[1];
+        const int64_t weight_n_experts = src0->ne[2];
+
+        int64_t weight_ne[3] = { weight_d, weight_m, weight_n_experts };
+        size_t  weight_nb[3] = { sizeof(int8_t), weight_d * sizeof(int8_t), weight_d * weight_m * sizeof(int8_t) };
+
+        acl_tensor_ptr all_weights =
+            ggml_cann_create_tensor(src0->data, ACL_INT8, sizeof(int8_t), weight_ne, weight_nb, 3);
+
+        int64_t selected_weight_ne[3] = { weight_d, weight_m, n_select_experts };
+        size_t  selected_weight_nb[3] = { sizeof(int8_t), weight_d * sizeof(int8_t),
+                                          weight_d * weight_m * sizeof(int8_t) };
+
+        acl_tensor_ptr selected_weights = ggml_cann_create_tensor(selected_weight_buffer, ACL_INT8, sizeof(int8_t),
+                                                                  selected_weight_ne, selected_weight_nb, 3);
+
+        GGML_CANN_CALL_ACLNN_OP(ctx, IndexSelect, all_weights.get(), 0, batch_indices.get(), selected_weights.get());
+
+        // Select scales using the same expert indices
+        const int64_t scale_d     = src0->ne[0] / group_size;
+        int64_t       scale_ne[3] = { scale_d, weight_m, weight_n_experts };
+        size_t scale_nb[3] = { scale_elem_size, scale_d * scale_elem_size, scale_d * weight_m * scale_elem_size };
+
+        acl_tensor_ptr all_scales =
+            ggml_cann_create_tensor(scale_data, ACL_FLOAT16, scale_elem_size, scale_ne, scale_nb, 3);
+
+        int64_t selected_scale_ne[3] = { scale_d, weight_m, n_select_experts };
+        size_t  selected_scale_nb[3] = { scale_elem_size, scale_d * scale_elem_size,
+                                         scale_d * weight_m * scale_elem_size };
+
+        acl_tensor_ptr selected_scales = ggml_cann_create_tensor(selected_scale_buffer, ACL_FLOAT16, scale_elem_size,
+                                                                 selected_scale_ne, selected_scale_nb, 3);
+
+        GGML_CANN_CALL_ACLNN_OP(ctx, IndexSelect, all_scales.get(), 0, batch_indices.get(), selected_scales.get());
+
+        // Process each expert for current batch
+        // IndexSelect output layout: [D, M, K] in contiguous format
+        // WeightQuantBatchMatmulV2 expects: [M, D] with row-major stride
+        for (int64_t expert_idx = 0; expert_idx < n_select_experts; expert_idx++) {
+            // Determine input offset: broadcast if src1->ne[1]==1, otherwise use per-expert input
+            const size_t input_offset =
+                (batch_idx * src1->ne[1] + (src1->ne[1] == 1 ? 0 : expert_idx)) * src1->ne[0] * f16_elem_size;
+            const size_t output_offset = (batch_idx * dst->ne[1] + expert_idx) * dst->ne[0] * f16_elem_size;
+
+            // Create weight view for current expert: [D, M, K] -> [M, D]
+            int64_t      weight_view_ne[2]  = { weight_m, src0->ne[0] };
+            float        weight_view_nb[2]  = { src0->ne[0] * weight_elem_size, weight_elem_size };
+            const size_t weight_view_offset = expert_idx * selected_weight_nb[2];
+
+            acl_tensor_ptr weight_view =
+                ggml_cann_create_tensor(selected_weight_buffer, ggml_cann_type_mapping(type), weight_elem_size,
+                                        weight_view_ne, weight_view_nb, 2, ACL_FORMAT_ND, weight_view_offset);
+
+            // Create scale view for current expert: [D, M, K] -> [M, D]
+            int64_t      scale_view_ne[2]  = { weight_m, scale_d };
+            size_t       scale_view_nb[2]  = { selected_scale_nb[1], selected_scale_nb[0] };
+            const size_t scale_view_offset = expert_idx * selected_scale_nb[2];
+
+            acl_tensor_ptr scale_view =
+                ggml_cann_create_tensor(selected_scale_buffer, ACL_FLOAT16, scale_elem_size, scale_view_ne,
+                                        scale_view_nb, 2, ACL_FORMAT_ND, scale_view_offset);
+
+            // Create input activation tensor [D, 1]
+            int64_t input_ne[2] = { src1->ne[0], 1 };
+            size_t  input_nb[2] = { f16_elem_size, src1->ne[0] * f16_elem_size };
+
+            acl_tensor_ptr input_tensor = ggml_cann_create_tensor(input_buffer, ACL_FLOAT16, f16_elem_size, input_ne,
+                                                                  input_nb, 2, ACL_FORMAT_ND, input_offset);
+
+            // Create output tensor [M, 1]
+            int64_t output_ne[2] = { dst->ne[0], 1 };
+            size_t  output_nb[2] = { f16_elem_size, dst->ne[0] * f16_elem_size };
+
+            acl_tensor_ptr output_tensor = ggml_cann_create_tensor(output_buffer, ACL_FLOAT16, f16_elem_size, output_ne,
+                                                                   output_nb, 2, ACL_FORMAT_ND, output_offset);
+
+            // Perform quantized matrix multiplication
+            GGML_CANN_CALL_ACLNN_OP(ctx, WeightQuantBatchMatmulV2, input_tensor.get(), weight_view.get(),
+                                    scale_view.get(), nullptr, nullptr, nullptr, nullptr, group_size,
+                                    output_tensor.get());
         }
     }
-    return;
+
+    // Cast output back to original type if we used a temporary F16 buffer
+    if (dst->type != GGML_TYPE_F16) {
+        int64_t ne[GGML_MAX_DIMS];
+        size_t  nb[GGML_MAX_DIMS] = { f16_elem_size };
+        for (int i = 0; i < GGML_MAX_DIMS; i++) {
+            ne[i] = dst->ne[i];
+            if (i > 0) {
+                nb[i] = nb[i - 1] * ne[i - 1];
+            }
+        }
+
+        acl_tensor_ptr f16_output =
+            ggml_cann_create_tensor(output_buffer, ACL_FLOAT16, f16_elem_size, ne, nb, GGML_MAX_DIMS);
+        acl_tensor_ptr dst_tensor = ggml_cann_create_tensor(dst);
+
+        aclnn_cast(ctx, f16_output.get(), dst_tensor.get(), ggml_cann_type_mapping(dst->type));
+    }
 }
 
 void ggml_cann_mul_mat_id(ggml_backend_cann_context & ctx, ggml_tensor * dst) {

ggml/src/ggml-cann/aclnn_ops.h

@@ -1,5 +1,5 @@
 /**
- * Copyright (c) 2023-2024 The ggml authors
+ * Copyright (c) 2023-2026 The ggml authors
  *
  * Permission is hereby granted, free of charge, to any person obtaining a copy
  * of this software and associated documentation files (the "Software"), to

ggml/src/ggml-cann/common.h

@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2023-2024 The ggml authors
+ * Copyright (c) 2023-2026 The ggml authors
  *
  * Permission is hereby granted, free of charge, to any person obtaining a copy
  * of this software and associated documentation files (the "Software"), to

ggml/src/ggml-cann/ggml-cann.cpp

@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2023-2024 The ggml authors
+ * Copyright (c) 2023-2026 The ggml authors
  *
  * Permission is hereby granted, free of charge, to any person obtaining a copy
  * of this software and associated documentation files (the "Software"), to
@@ -794,19 +794,44 @@ struct ggml_backend_cann_buffer_context {
     ~ggml_backend_cann_buffer_context() { ACL_CHECK(aclrtFree(dev_ptr)); }
 };
 
+// cann buffer type
 /**
- * @brief Check if a buffer is a CANN buffer.
- *
- * This function checks if a given buffer is a CANN buffer by comparing its
- * `get_name` function pointer to `ggml_backend_cann_buffer_get_name`.
- *
- * @param buffer The buffer to check.
- * @return true if the buffer is a CANN buffer, false otherwise.
+ * @brief Structure representing context information for a specific backend
+ * buffer type.
  */
-static bool ggml_backend_buft_is_cann(ggml_backend_buffer_type_t buft);
+struct ggml_backend_cann_buffer_type_context {
+    int32_t     device; /**< Device identifier associated with the buffer context. */
+    std::string name;   /**< Name associated with the buffer context. */
+};
 
-static bool ggml_backend_buffer_is_cann(ggml_backend_buffer_t buffer) {
-    return ggml_backend_buft_is_cann(buffer->buft);
+/**
+ * @brief Retrieves the name associated with a CANN buffer type.
+ *
+ * This function returns the descriptive name associated with the specified
+ * CANN buffer type context.
+ *
+ * @param buft Pointer to the buffer type context.
+ * @return Const pointer to the C-style string containing the name.
+ */
+static const char * ggml_backend_cann_buffer_type_name(ggml_backend_buffer_type_t buft) {
+    ggml_backend_cann_buffer_type_context * buft_ctx = (ggml_backend_cann_buffer_type_context *) buft->context;
+
+    return buft_ctx->name.c_str();
+}
+
+/**
+ * @brief Checks if the backend buffer type is associated with the CANN backend.
+ *
+ * This function checks whether the provided backend buffer type is associated
+ * with the CANN backend based on the comparison of its name retrieval function
+ * pointer.
+ *
+ * @param buft Pointer to the backend buffer type to check.
+ * @return bool Returns true if the buffer type is associated with the CANN
+ * backend, otherwise false.
+ */
+static bool ggml_backend_buft_is_cann(ggml_backend_buffer_type_t buft) {
+    return buft->iface.get_name == ggml_backend_cann_buffer_type_name;
 }
 
 /**
@@ -1271,7 +1296,7 @@ static void ggml_backend_cann_buffer_get_tensor(ggml_backend_buffer_t buffer,
 static bool ggml_backend_cann_buffer_cpy_tensor(ggml_backend_buffer_t buffer,
                                                 const ggml_tensor *   src,
                                                 ggml_tensor *         dst) {
-    if (ggml_backend_buffer_is_cann(src->buffer)) {
+    if (ggml_backend_buft_is_cann(src->buffer->buft)) {
         ggml_backend_cann_buffer_context * src_ctx = (ggml_backend_cann_buffer_context *) src->buffer->context;
         ggml_backend_cann_buffer_context * dst_ctx = (ggml_backend_cann_buffer_context *) buffer->context;
 
@@ -1330,36 +1355,13 @@ static const ggml_backend_buffer_i ggml_backend_cann_buffer_interface = {
     /* .memset_tensor   = */ NULL,
     /* .set_tensor      = */ ggml_backend_cann_buffer_set_tensor,
     /* .get_tensor      = */ ggml_backend_cann_buffer_get_tensor,
+    /* .set_tensor_2d   = */ NULL,
+    /* .get_tensor_2d   = */ NULL,
     /* .cpy_tensor      = */ ggml_backend_cann_buffer_cpy_tensor,
     /* .clear           = */ ggml_backend_cann_buffer_clear,
     /* .reset           = */ NULL,
 };
 
-// cann buffer type
-/**
- * @brief Structure representing context information for a specific backend
- * buffer type.
- */
-struct ggml_backend_cann_buffer_type_context {
-    int32_t     device; /**< Device identifier associated with the buffer context. */
-    std::string name;   /**< Name associated with the buffer context. */
-};
-
-/**
- * @brief Retrieves the name associated with a CANN buffer type.
- *
- * This function returns the descriptive name associated with the specified
- * CANN buffer type context.
- *
- * @param buft Pointer to the buffer type context.
- * @return Const pointer to the C-style string containing the name.
- */
-static const char * ggml_backend_cann_buffer_type_name(ggml_backend_buffer_type_t buft) {
-    ggml_backend_cann_buffer_type_context * buft_ctx = (ggml_backend_cann_buffer_type_context *) buft->context;
-
-    return buft_ctx->name.c_str();
-}
-
 /**
  * @brief Allocates a new CANN buffer of the specified type and size.
  *
@@ -1997,7 +1999,7 @@ static bool ggml_backend_cann_cpy_tensor_async(ggml_backend_t      backend_src,
 
     GGML_ASSERT(!is_matmul_weight((const ggml_tensor *) src));
 
-    if (!ggml_backend_buffer_is_cann(src->buffer) || !ggml_backend_buffer_is_cann(dst->buffer)) {
+    if (!ggml_backend_buft_is_cann(src->buffer->buft) || !ggml_backend_buft_is_cann(dst->buffer->buft)) {
         return false;
     }
 
@@ -2523,21 +2525,6 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev, const ggml_ten
     GGML_UNUSED(dev);
 }
 
-/**
- * @brief Checks if the backend buffer type is associated with the CANN backend.
- *
- * This function checks whether the provided backend buffer type is associated
- * with the CANN backend based on the comparison of its name retrieval function
- * pointer.
- *
- * @param buft Pointer to the backend buffer type to check.
- * @return bool Returns true if the buffer type is associated with the CANN
- * backend, otherwise false.
- */
-static bool ggml_backend_buft_is_cann(ggml_backend_buffer_type_t buft) {
-    return buft->iface.get_name == ggml_backend_cann_buffer_type_name;
-}
-
 /**
  * @brief Records an event on the CANN backend stream.
  *
@@ -2582,6 +2569,8 @@ static const ggml_backend_i ggml_backend_cann_interface = {
     /* .free                    = */ ggml_backend_cann_free,
     /* .set_tensor_async        = */ ggml_backend_cann_set_tensor_async,
     /* .get_tensor_async        = */ ggml_backend_cann_get_tensor_async,
+    /* .get_tensor_2d_async     = */ NULL,
+    /* .set_tensor_2d_async     = */ NULL,
     /* .cpy_tensor_async        = */ ggml_backend_cann_cpy_tensor_async,
     /* .synchronize             = */ ggml_backend_cann_synchronize,
     /* .graph_plan_create       = */ NULL,

ggml/src/ggml-cpu/arch-fallback.h

@@ -43,6 +43,7 @@
 #define ggml_gemv_q4_K_8x4_q8_K_generic ggml_gemv_q4_K_8x4_q8_K
 #define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
 #define ggml_gemv_q5_K_8x8_q8_K_generic ggml_gemv_q5_K_8x8_q8_K
+#define ggml_gemv_q6_K_8x4_q8_K_generic ggml_gemv_q6_K_8x4_q8_K
 #define ggml_gemv_q6_K_8x8_q8_K_generic ggml_gemv_q6_K_8x8_q8_K
 #define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
 #define ggml_gemv_iq4_nl_8x8_q8_0_generic ggml_gemv_iq4_nl_8x8_q8_0
@@ -55,7 +56,8 @@
 #define ggml_gemm_q4_K_8x4_q8_K_generic ggml_gemm_q4_K_8x4_q8_K
 #define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
 #define ggml_gemm_q5_K_8x8_q8_K_generic ggml_gemm_q5_K_8x8_q8_K
-#    define ggml_gemm_q6_K_8x8_q8_K_generic   ggml_gemm_q6_K_8x8_q8_K
+#define ggml_gemm_q6_K_8x4_q8_K_generic ggml_gemm_q6_K_8x4_q8_K
+#define ggml_gemm_q6_K_8x8_q8_K_generic   ggml_gemm_q6_K_8x8_q8_K
 #define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
 #define ggml_gemm_iq4_nl_8x8_q8_0_generic ggml_gemm_iq4_nl_8x8_q8_0
 #define ggml_gemm_q8_0_4x4_q8_0_generic ggml_gemm_q8_0_4x4_q8_0
@@ -76,6 +78,7 @@
 #define ggml_gemv_q4_0_4x8_q8_0_generic ggml_gemv_q4_0_4x8_q8_0
 #define ggml_gemv_q4_K_8x4_q8_K_generic ggml_gemv_q4_K_8x4_q8_K
 #define ggml_gemv_q5_K_8x8_q8_K_generic ggml_gemv_q5_K_8x8_q8_K
+#define ggml_gemv_q6_K_8x4_q8_K_generic ggml_gemv_q6_K_8x4_q8_K
 #define ggml_gemv_q6_K_8x8_q8_K_generic ggml_gemv_q6_K_8x8_q8_K
 #define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
 #define ggml_gemv_q8_0_4x4_q8_0_generic ggml_gemv_q8_0_4x4_q8_0
@@ -84,6 +87,7 @@
 #define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0
 #define ggml_gemm_q4_K_8x4_q8_K_generic ggml_gemm_q4_K_8x4_q8_K
 #define ggml_gemm_q5_K_8x8_q8_K_generic ggml_gemm_q5_K_8x8_q8_K
+#define ggml_gemm_q6_K_8x4_q8_K_generic ggml_gemm_q6_K_8x4_q8_K
 #define ggml_gemm_q6_K_8x8_q8_K_generic ggml_gemm_q6_K_8x8_q8_K
 #define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
 #define ggml_gemm_q8_0_4x4_q8_0_generic ggml_gemm_q8_0_4x4_q8_0
@@ -107,6 +111,7 @@
 #define ggml_gemv_q4_K_8x4_q8_K_generic ggml_gemv_q4_K_8x4_q8_K
 #define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
 #define ggml_gemv_q5_K_8x8_q8_K_generic ggml_gemv_q5_K_8x8_q8_K
+#define ggml_gemv_q6_K_8x4_q8_K_generic ggml_gemv_q6_K_8x4_q8_K
 #define ggml_gemv_q6_K_8x8_q8_K_generic ggml_gemv_q6_K_8x8_q8_K
 #define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
 #define ggml_gemv_iq4_nl_8x8_q8_0_generic ggml_gemv_iq4_nl_8x8_q8_0
@@ -119,6 +124,7 @@
 #define ggml_gemm_q4_K_8x4_q8_K_generic ggml_gemm_q4_K_8x4_q8_K
 #define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
 #define ggml_gemm_q5_K_8x8_q8_K_generic ggml_gemm_q5_K_8x8_q8_K
+#define ggml_gemm_q6_K_8x4_q8_K_generic ggml_gemm_q6_K_8x4_q8_K
 #define ggml_gemm_q6_K_8x8_q8_K_generic ggml_gemm_q6_K_8x8_q8_K
 #define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
 #define ggml_gemm_iq4_nl_8x8_q8_0_generic ggml_gemm_iq4_nl_8x8_q8_0
@@ -143,6 +149,7 @@
 #define ggml_gemv_q4_K_8x4_q8_K_generic ggml_gemv_q4_K_8x4_q8_K
 #define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
 #define ggml_gemv_q5_K_8x8_q8_K_generic ggml_gemv_q5_K_8x8_q8_K
+#define ggml_gemv_q6_K_8x4_q8_K_generic ggml_gemv_q6_K_8x4_q8_K
 #define ggml_gemv_q6_K_8x8_q8_K_generic ggml_gemv_q6_K_8x8_q8_K
 #define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
 #define ggml_gemv_iq4_nl_8x8_q8_0_generic ggml_gemv_iq4_nl_8x8_q8_0
@@ -155,6 +162,7 @@
 #define ggml_gemm_q4_K_8x4_q8_K_generic ggml_gemm_q4_K_8x4_q8_K
 #define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
 #define ggml_gemm_q5_K_8x8_q8_K_generic ggml_gemm_q5_K_8x8_q8_K
+#define ggml_gemm_q6_K_8x4_q8_K_generic ggml_gemm_q6_K_8x4_q8_K
 #define ggml_gemm_q6_K_8x8_q8_K_generic ggml_gemm_q6_K_8x8_q8_K
 #define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
 #define ggml_gemm_iq4_nl_8x8_q8_0_generic ggml_gemm_iq4_nl_8x8_q8_0
@@ -186,6 +194,7 @@
 #define ggml_gemv_q4_K_8x4_q8_K_generic ggml_gemv_q4_K_8x4_q8_K
 #define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
 #define ggml_gemv_q5_K_8x8_q8_K_generic ggml_gemv_q5_K_8x8_q8_K
+#define ggml_gemv_q6_K_8x4_q8_K_generic ggml_gemv_q6_K_8x4_q8_K
 #define ggml_gemv_q6_K_8x8_q8_K_generic ggml_gemv_q6_K_8x8_q8_K
 #define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
 #define ggml_gemv_iq4_nl_8x8_q8_0_generic ggml_gemv_iq4_nl_8x8_q8_0
@@ -197,6 +206,7 @@
 #define ggml_gemm_q4_K_8x4_q8_K_generic ggml_gemm_q4_K_8x4_q8_K
 #define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
 #define ggml_gemm_q5_K_8x8_q8_K_generic ggml_gemm_q5_K_8x8_q8_K
+#define ggml_gemm_q6_K_8x4_q8_K_generic ggml_gemm_q6_K_8x4_q8_K
 #define ggml_gemm_q6_K_8x8_q8_K_generic ggml_gemm_q6_K_8x8_q8_K
 #define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
 #define ggml_gemm_iq4_nl_8x8_q8_0_generic ggml_gemm_iq4_nl_8x8_q8_0
@@ -227,6 +237,7 @@
 #define ggml_gemv_q4_K_8x4_q8_K_generic ggml_gemv_q4_K_8x4_q8_K
 #define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
 #define ggml_gemv_q5_K_8x8_q8_K_generic ggml_gemv_q5_K_8x8_q8_K
+#define ggml_gemv_q6_K_8x4_q8_K_generic ggml_gemv_q6_K_8x4_q8_K
 #define ggml_gemv_q6_K_8x8_q8_K_generic ggml_gemv_q6_K_8x8_q8_K
 #define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
 #define ggml_gemv_iq4_nl_8x8_q8_0_generic ggml_gemv_iq4_nl_8x8_q8_0
@@ -239,6 +250,7 @@
 #define ggml_gemm_q4_K_8x4_q8_K_generic ggml_gemm_q4_K_8x4_q8_K
 #define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
 #define ggml_gemm_q5_K_8x8_q8_K_generic ggml_gemm_q5_K_8x8_q8_K
+#define ggml_gemm_q6_K_8x4_q8_K_generic ggml_gemm_q6_K_8x4_q8_K
 #define ggml_gemm_q6_K_8x8_q8_K_generic ggml_gemm_q6_K_8x8_q8_K
 #define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
 #define ggml_gemm_iq4_nl_8x8_q8_0_generic ggml_gemm_iq4_nl_8x8_q8_0
@@ -271,6 +283,7 @@
 #define ggml_gemv_q4_K_8x4_q8_K_generic ggml_gemv_q4_K_8x4_q8_K
 #define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
 #define ggml_gemv_q5_K_8x8_q8_K_generic ggml_gemv_q5_K_8x8_q8_K
+#define ggml_gemv_q6_K_8x4_q8_K_generic ggml_gemv_q6_K_8x4_q8_K
 #define ggml_gemv_q6_K_8x8_q8_K_generic ggml_gemv_q6_K_8x8_q8_K
 #define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
 #define ggml_gemv_iq4_nl_8x8_q8_0_generic ggml_gemv_iq4_nl_8x8_q8_0
@@ -283,6 +296,7 @@
 #define ggml_gemm_q4_K_8x4_q8_K_generic ggml_gemm_q4_K_8x4_q8_K
 #define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
 #define ggml_gemm_q5_K_8x8_q8_K_generic ggml_gemm_q5_K_8x8_q8_K
+#define ggml_gemm_q6_K_8x4_q8_K_generic ggml_gemm_q6_K_8x4_q8_K
 #define ggml_gemm_q6_K_8x8_q8_K_generic ggml_gemm_q6_K_8x8_q8_K
 #define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
 #define ggml_gemm_iq4_nl_8x8_q8_0_generic ggml_gemm_iq4_nl_8x8_q8_0