llama.cpp/docs/build.md

Build llama.cpp locally

To get the Code:

git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp

In order to build llama.cpp you have four different options.

• Using make:

  • On Linux or MacOS:

    make
    

  • On Windows:

    1. Download the latest fortran version of w64devkit.
    2. Extract w64devkit on your pc.
    3. Run w64devkit.exe.
    4. Use the cd command to reach the llama.cpp folder.
    5. From here you can run:

       make
       

  • Notes:

    • For Q4_0_4_4 quantization type build, add the GGML_NO_LLAMAFILE=1 flag. For example, use make GGML_NO_LLAMAFILE=1.
    • For faster compilation, add the -j argument to run multiple jobs in parallel. For example, make -j 8 will run 8 jobs in parallel.
    • For faster repeated compilation, install ccache.
    • For debug builds, run make LLAMA_DEBUG=1

• Using CMake:

  cmake -B build
  cmake --build build --config Release
  

  Notes:

  • For Q4_0_4_4 quantization type build, add the -DGGML_LLAMAFILE=OFF cmake option. For example, use cmake -B build -DGGML_LLAMAFILE=OFF.

  • For faster compilation, add the -j argument to run multiple jobs in parallel. For example, cmake --build build --config Release -j 8 will run 8 jobs in parallel.

  • For faster repeated compilation, install ccache.

  • For debug builds, there are two cases:

    1. Single-config generators (e.g. default = Unix Makefiles; note that they just ignore the --config flag):

    cmake -B build -DCMAKE_BUILD_TYPE=Debug
    cmake --build build
    

    2. Multi-config generators (-G param set to Visual Studio, XCode...):

    cmake -B build -G "Xcode"
    cmake --build build --config Debug
    

• Using gmake (FreeBSD):

  1. Install and activate DRM in FreeBSD

  2. Add your user to video group

  3. Install compilation dependencies.

     sudo pkg install gmake automake autoconf pkgconf llvm15 openblas
     
     gmake CC=/usr/local/bin/clang15 CXX=/usr/local/bin/clang++15 -j4
     

Metal Build

On MacOS, Metal is enabled by default. Using Metal makes the computation run on the GPU. To disable the Metal build at compile time use the GGML_NO_METAL=1 flag or the GGML_METAL=OFF cmake option.

When built with Metal support, you can explicitly disable GPU inference with the --n-gpu-layers|-ngl 0 command-line argument.

BLAS Build

Building the program with BLAS support may lead to some performance improvements in prompt processing using batch sizes higher than 32 (the default is 512). Support with CPU-only BLAS implementations doesn't affect the normal generation performance. We may see generation performance improvements with GPU-involved BLAS implementations, e.g. cuBLAS, hipBLAS. There are currently several different BLAS implementations available for build and use:

Accelerate Framework:

This is only available on Mac PCs and it's enabled by default. You can just build using the normal instructions.

OpenBLAS:

This provides BLAS acceleration using only the CPU. Make sure to have OpenBLAS installed on your machine.

• Using make:

  • On Linux:

    make GGML_OPENBLAS=1
    

  • On Windows:

    1. Download the latest fortran version of w64devkit.

    2. Download the latest version of OpenBLAS for Windows.

    3. Extract w64devkit on your pc.

    4. From the OpenBLAS zip that you just downloaded copy libopenblas.a, located inside the lib folder, inside w64devkit\x86_64-w64-mingw32\lib.

    5. From the same OpenBLAS zip copy the content of the include folder inside w64devkit\x86_64-w64-mingw32\include.

    6. Run w64devkit.exe.

    7. Use the cd command to reach the llama.cpp folder.

    8. From here you can run:

       make GGML_OPENBLAS=1
       

• Using CMake on Linux:

  cmake -B build -DGGML_BLAS=ON -DGGML_BLAS_VENDOR=OpenBLAS
  cmake --build build --config Release
  

BLIS

Check BLIS.md for more information.

SYCL

SYCL is a higher-level programming model to improve programming productivity on various hardware accelerators.

llama.cpp based on SYCL is used to support Intel GPU (Data Center Max series, Flex series, Arc series, Built-in GPU and iGPU).

For detailed info, please refer to llama.cpp for SYCL.

Intel oneMKL

Building through oneAPI compilers will make avx_vnni instruction set available for intel processors that do not support avx512 and avx512_vnni. Please note that this build config does not support Intel GPU. For Intel GPU support, please refer to llama.cpp for SYCL.

• Using manual oneAPI installation: By default, GGML_BLAS_VENDOR is set to Generic, so if you already sourced intel environment script and assign -DGGML_BLAS=ON in cmake, the mkl version of Blas will automatically been selected. Otherwise please install oneAPI and follow the below steps:

  source /opt/intel/oneapi/setvars.sh # You can skip this step if  in oneapi-basekit docker image, only required for manual installation
  cmake -B build -DGGML_BLAS=ON -DGGML_BLAS_VENDOR=Intel10_64lp -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_NATIVE=ON
  cmake --build build --config Release
  

• Using oneAPI docker image: If you do not want to source the environment vars and install oneAPI manually, you can also build the code using intel docker container: oneAPI-basekit. Then, you can use the commands given above.

Check Optimizing and Running LLaMA2 on Intel® CPU for more information.

CUDA

This provides GPU acceleration using the CUDA cores of your Nvidia GPU. Make sure to have the CUDA toolkit installed. You can download it from your Linux distro's package manager (e.g. apt install nvidia-cuda-toolkit) or from here: CUDA Toolkit.

For Jetson user, if you have Jetson Orin, you can try this: Offical Support. If you are using an old model(nano/TX2), need some additional operations before compiling.

• Using make:

  make GGML_CUDA=1
  

• Using CMake:

  cmake -B build -DGGML_CUDA=ON
  cmake --build build --config Release
  

The environment variable CUDA_VISIBLE_DEVICES can be used to specify which GPU(s) will be used. The following compilation options are also available to tweak performance:

Option	Legal values	Default	Description
GGML_CUDA_FORCE_DMMV	Boolean	false	Force the use of dequantization + matrix vector multiplication kernels instead of using kernels that do matrix vector multiplication on quantized data. By default the decision is made based on compute capability (MMVQ for 6.1/Pascal/GTX 1000 or higher). Does not affect k-quants.
GGML_CUDA_DMMV_X	Positive integer >= 32	32	Number of values in x direction processed by the CUDA dequantization + matrix vector multiplication kernel per iteration. Increasing this value can improve performance on fast GPUs. Power of 2 heavily recommended. Does not affect k-quants.
GGML_CUDA_MMV_Y	Positive integer	1	Block size in y direction for the CUDA mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended.
GGML_CUDA_FORCE_MMQ	Boolean	false	Force the use of custom matrix multiplication kernels for quantized models instead of FP16 cuBLAS even if there is no int8 tensor core implementation available (affects V100, RDNA3). MMQ kernels are enabled by default on GPUs with int8 tensor core support. With MMQ force enabled, speed for large batch sizes will be worse but VRAM consumption will be lower.
GGML_CUDA_FORCE_CUBLAS	Boolean	false	Force the use of FP16 cuBLAS instead of custom matrix multiplication kernels for quantized models
GGML_CUDA_F16	Boolean	false	If enabled, use half-precision floating point arithmetic for the CUDA dequantization + mul mat vec kernels and for the q4_1 and q5_1 matrix matrix multiplication kernels. Can improve performance on relatively recent GPUs.
GGML_CUDA_KQUANTS_ITER	1 or 2	2	Number of values processed per iteration and per CUDA thread for Q2_K and Q6_K quantization formats. Setting this value to 1 can improve performance for slow GPUs.
GGML_CUDA_PEER_MAX_BATCH_SIZE	Positive integer	128	Maximum batch size for which to enable peer access between multiple GPUs. Peer access requires either Linux or NVLink. When using NVLink enabling peer access for larger batch sizes is potentially beneficial.
GGML_CUDA_FA_ALL_QUANTS	Boolean	false	Compile support for all KV cache quantization type (combinations) for the FlashAttention CUDA kernels. More fine-grained control over KV cache size but compilation takes much longer.

hipBLAS

This provides BLAS acceleration on HIP-supported AMD GPUs. Make sure to have ROCm installed. You can download it from your Linux distro's package manager or from here: ROCm Quick Start (Linux).

• Using make:

  make GGML_HIPBLAS=1
  

• Using CMake for Linux (assuming a gfx1030-compatible AMD GPU):

  HIPCXX="$(hipconfig -l)/clang" HIP_PATH="$(hipconfig -R)" \
      cmake -S . -B build -DGGML_HIPBLAS=ON -DAMDGPU_TARGETS=gfx1030 -DCMAKE_BUILD_TYPE=Release \
      && cmake --build build --config Release -- -j 16
  

  On Linux it is also possible to use unified memory architecture (UMA) to share main memory between the CPU and integrated GPU by setting -DGGML_HIP_UMA=ON. However, this hurts performance for non-integrated GPUs (but enables working with integrated GPUs).

  Note that if you get the following error:

  clang: error: cannot find ROCm device library; provide its path via '--rocm-path' or '--rocm-device-lib-path', or pass '-nogpulib' to build without ROCm device library
  

  Try searching for a directory under HIP_PATH that contains the file oclc_abi_version_400.bc. Then, add the following to the start of the command: HIP_DEVICE_LIB_PATH=<directory-you-just-found>, so something like:

  HIPCXX="$(hipconfig -l)/clang" HIP_PATH="$(hipconfig -p)" \
  HIP_DEVICE_LIB_PATH=<directory-you-just-found> \
      cmake -S . -B build -DGGML_HIPBLAS=ON -DAMDGPU_TARGETS=gfx1030 -DCMAKE_BUILD_TYPE=Release \
      && cmake --build build -- -j 16
  

• Using make (example for target gfx1030, build with 16 CPU threads):

  make -j16 GGML_HIPBLAS=1 GGML_HIP_UMA=1 AMDGPU_TARGETS=gfx1030
  

• Using CMake for Windows (using x64 Native Tools Command Prompt for VS, and assuming a gfx1100-compatible AMD GPU):

  set PATH=%HIP_PATH%\bin;%PATH%
  cmake -S . -B build -G Ninja -DAMDGPU_TARGETS=gfx1100 -DGGML_HIPBLAS=ON -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ -DCMAKE_BUILD_TYPE=Release
  cmake --build build
  

  Make sure that AMDGPU_TARGETS is set to the GPU arch you want to compile for. The above example uses gfx1100 that corresponds to Radeon RX 7900XTX/XT/GRE. You can find a list of targets here Find your gpu version string by matching the most significant version information from rocminfo | grep gfx | head -1 | awk '{print $2}' with the list of processors, e.g. gfx1035 maps to gfx1030.

The environment variable HIP_VISIBLE_DEVICES can be used to specify which GPU(s) will be used. If your GPU is not officially supported you can use the environment variable [HSA_OVERRIDE_GFX_VERSION] set to a similar GPU, for example 10.3.0 on RDNA2 (e.g. gfx1030, gfx1031, or gfx1035) or 11.0.0 on RDNA3. The following compilation options are also available to tweak performance (yes, they refer to CUDA, not HIP, because it uses the same code as the cuBLAS version above):

Option	Legal values	Default	Description
GGML_CUDA_DMMV_X	Positive integer >= 32	32	Number of values in x direction processed by the HIP dequantization + matrix vector multiplication kernel per iteration. Increasing this value can improve performance on fast GPUs. Power of 2 heavily recommended. Does not affect k-quants.
GGML_CUDA_MMV_Y	Positive integer	1	Block size in y direction for the HIP mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. Does not affect k-quants.
GGML_CUDA_KQUANTS_ITER	1 or 2	2	Number of values processed per iteration and per HIP thread for Q2_K and Q6_K quantization formats. Setting this value to 1 can improve performance for slow GPUs.

Vulkan

With docker:

You don't need to install Vulkan SDK. It will be installed inside the container.

# Build the image
docker build -t llama-cpp-vulkan -f .devops/llama-cli-vulkan.Dockerfile .

# Then, use it:
docker run -it --rm -v "$(pwd):/app:Z" --device /dev/dri/renderD128:/dev/dri/renderD128 --device /dev/dri/card1:/dev/dri/card1 llama-cpp-vulkan -m "/app/models/YOUR_MODEL_FILE" -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33

Without docker:

Firstly, you need to make sure you have installed Vulkan SDK

For example, on Ubuntu 22.04 (jammy), use the command below:

wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add -
wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list
apt update -y
apt-get install -y vulkan-sdk
# To verify the installation, use the command below:
vulkaninfo

Alternatively your package manager might be able to provide the appropriate libraries. For example for Ubuntu 22.04 you can install libvulkan-dev instead. For Fedora 40, you can install vulkan-devel, glslc and glslang packages.

Then, build llama.cpp using the cmake command below:

cmake -B build -DGGML_VULKAN=1
cmake --build build --config Release
# Test the output binary (with "-ngl 33" to offload all layers to GPU)
./bin/llama-cli -m "PATH_TO_MODEL" -p "Hi you how are you" -n 50 -e -ngl 33 -t 4

# You should see in the output, ggml_vulkan detected your GPU. For example:
# ggml_vulkan: Using Intel(R) Graphics (ADL GT2) | uma: 1 | fp16: 1 | warp size: 32

Android

To read documentation for how to build on Android, click here