Welcome to the installation guide for the bitsandbytes library! This document provides step-by-step instructions to install bitsandbytes across various platforms and hardware configurations.
We provide official support for NVIDIA GPUs, CPUs, Intel XPUs, and Intel Gaudi platforms. We also have experimental support for additional platforms such as AMD ROCm.
These are the minimum requirements for bitsandbytes across all platforms. Please be aware that some compute platforms may impose more strict requirements.
- Python >= 3.10
- PyTorch >= 2.3
bitsandbytes is currently supported on NVIDIA GPUs with Compute Capability 6.0+.
The library can be built using CUDA Toolkit versions as old as 11.8.
| Feature | CC Required | Example Hardware Requirement |
|---|---|---|
| LLM.int8() | 7.5+ | Turing (RTX 20 series, T4) or newer GPUs |
| 8-bit optimizers/quantization | 6.0+ | Pascal (GTX 10X0 series, P100) or newer GPUs |
| NF4/FP4 quantization | 6.0+ | Pascal (GTX 10X0 series, P100) or newer GPUs |
This is the most straightforward and recommended installation option.
The currently distributed bitsandbytes packages are built with the following configurations:
| OS | CUDA Toolkit | Host Compiler | Targets |
|---|---|---|---|
| Linux x86-64 | 11.8 - 12.6 | GCC 11.2 | sm60, sm70, sm75, sm80, sm86, sm89, sm90 |
| Linux x86-64 | 12.8 - 12.9 | GCC 11.2 | sm70, sm75, sm80, sm86, sm89, sm90, sm100, sm120 |
| Linux x86-64 | 13.0 | GCC 11.2 | sm75, sm80, sm86, sm89, sm90, sm100, sm110, sm120 |
| Linux aarch64 | 11.8 - 12.6 | GCC 11.2 | sm75, sm80, sm90 |
| Linux aarch64 | 12.8 - 13.0 | GCC 11.2 | sm75, sm80, sm90, sm100, sm120 |
| Windows x86-64 | 11.8 - 12.6 | MSVC 19.43+ (VS2022) | sm50, sm60, sm75, sm80, sm86, sm89, sm90 |
| Windows x86-64 | 12.8 - 12.9 | MSVC 19.43+ (VS2022) | sm70, sm75, sm80, sm86, sm89, sm90, sm100, sm120 |
| Windows x86-64 | 13.0 | MSVC 19.43+ (VS2022) | sm75, sm80, sm86, sm89, sm90, sm100, sm120 |
The Linux build has a minimum glibc version of 2.24.
Use pip or uv to install the latest release:
pip install bitsandbytesTip
Don't hesitate to compile from source! The process is pretty straight forward and resilient. This might be needed for older CUDA Toolkit versions or Linux distributions, or other less common configurations.
For Linux and Windows systems, compiling from source allows you to customize the build configurations. See below for detailed platform-specific instructions (see the CMakeLists.txt if you want to check the specifics and explore some additional options):
To compile from source, you need CMake >= 3.22.1 and Python >= 3.10 installed. Make sure you have a compiler installed to compile C++ (gcc, make, headers, etc.). It is recommended to use GCC 11 or newer.
For example, to install a compiler and CMake on Ubuntu:
apt-get install -y build-essential cmakeYou should also install CUDA Toolkit by following the NVIDIA CUDA Installation Guide for Linux guide. The current minimum supported CUDA Toolkit version that we support is 11.8.
git clone https://github.com/bitsandbytes-foundation/bitsandbytes.git && cd bitsandbytes/
cmake -DCOMPUTE_BACKEND=cuda -S .
make
pip install -e . # `-e` for "editable" install, when developing BNB (otherwise leave that out)Tip
If you have multiple versions of the CUDA Toolkit installed or it is in a non-standard location, please refer to CMake CUDA documentation for how to configure the CUDA compiler.
Compilation from source on Windows systems require Visual Studio with C++ support as well as an installation of the CUDA Toolkit.
To compile from source, you need CMake >= 3.22.1 and Python >= 3.9 installed. You should also install CUDA Toolkit by following the CUDA Installation Guide for Windows guide from NVIDIA. The current minimum supported CUDA Toolkit version that we support is 11.8.
git clone https://github.com/bitsandbytes-foundation/bitsandbytes.git && cd bitsandbytes/
cmake -DCOMPUTE_BACKEND=cuda -S .
cmake --build . --config Release
pip install -e . # `-e` for "editable" install, when developing BNB (otherwise leave that out)Big thanks to wkpark, Jamezo97, rickardp, akx for their amazing contributions to make bitsandbytes compatible with Windows.
- A compatible PyTorch version with Intel XPU support is required. The current minimum is PyTorch 2.6.0. It is recommended to use the latest stable release. See Getting Started on Intel GPU for guidance.
This is the most straightforward and recommended installation option.
The currently distributed bitsandbytes packages are built with the following configurations:
| OS | oneAPI Toolkit | Kernel Implementation |
|---|---|---|
| Linux x86-64 | 2025.1.3 | SYCL + Triton |
| Windows x86-64 | 2025.1.3 | SYCL + Triton |
The Linux build has a minimum glibc version of 2.34.
Use pip or uv to install the latest release:
pip install bitsandbytes- A compatible PyTorch version with Intel Gaudi support is required. The current minimum is Gaudi v1.21 with PyTorch 2.6.0. It is recommended to use the latest stable release. See the Gaudi software installation guide for guidance.
Use pip or uv to install the latest release:
pip install bitsandbytesThis is the most straightforward and recommended installation option.
The currently distributed bitsandbytes packages are built with the following configurations:
| OS | Host Compiler | Hardware Minimum |
|---|---|---|
| Linux x86-64 | GCC 11.4 | AVX2 |
| Linux aarch64 | GCC 11.4 | |
| Windows x86-64 | MSVC 19.43+ (VS2022) | AVX2 |
The Linux build has a minimum glibc version of 2.24.
Use pip or uv to install the latest release:
pip install bitsandbytesTo compile from source, simply install the package from source using pip. The package will be built for CPU only at this time.
git clone https://github.com/bitsandbytes-foundation/bitsandbytes.git && cd bitsandbytes/
pip install -e .- A compatible PyTorch version with AMD ROCm support is required. It is recommended to use the latest stable release. See PyTorch on ROCm for guidance.
- ROCm support is currently only available in our preview wheels or when building from source.
The currently distributed preview bitsandbytes are built with the following configurations:
| OS | ROCm | Targets |
|---|---|---|
| Linux x86-64 | 6.2.4 | CDNA: gfx90a, gfx942 / RDNA: gfx1100, gfx1101 |
| Linux x86-64 | 6.3.4 | CDNA: gfx90a, gfx942 / RDNA: gfx1100, gfx1101 |
| Linux x86-64 | 6.4.4 | CDNA: gfx90a, gfx942 / RDNA: gfx1100, gfx1101, gfx1200, gfx1201 |
| Linux x86-64 | 7.0.2 | CDNA: gfx90a, gfx942, gfx950 / RDNA: gfx1100 / gfx1101 / gfx1200 / gfx1201 |
Windows is not currently supported.
Please see Preview Wheels for installation instructions.
bitsandbytes can be compiled from ROCm 6.1 - ROCm 7.0.
# Install bitsandbytes from source
# Clone bitsandbytes repo
git clone https://github.com/bitsandbytes-foundation/bitsandbytes.git && cd bitsandbytes/
# Compile & install
apt-get install -y build-essential cmake # install build tools dependencies, unless present
cmake -DCOMPUTE_BACKEND=hip -S . # Use -DBNB_ROCM_ARCH="gfx90a;gfx942" to target specific gpu arch
make
pip install -e . # `-e` for "editable" install, when developing BNB (otherwise leave that out)If you would like to use new features even before they are officially released and help us test them, feel free to install the wheel directly from our CI (the wheel links will remain stable!):
# Note: if you don't want to reinstall our dependencies, append the `--no-deps` flag!
# x86_64 (most users)
pip install --force-reinstall https://github.com/bitsandbytes-foundation/bitsandbytes/releases/download/continuous-release_main/bitsandbytes-1.33.7.preview-py3-none-manylinux_2_24_x86_64.whl
# ARM/aarch64
pip install --force-reinstall https://github.com/bitsandbytes-foundation/bitsandbytes/releases/download/continuous-release_main/bitsandbytes-1.33.7.preview-py3-none-manylinux_2_24_aarch64.whl# Note: if you don't want to reinstall our dependencies, append the `--no-deps` flag!
pip install --force-reinstall https://github.com/bitsandbytes-foundation/bitsandbytes/releases/download/continuous-release_main/bitsandbytes-1.33.7.preview-py3-none-win_amd64.whl