Bitsandbytes documentation
Installation Guide
Installation Guide
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. The library primarily supports CUDA-based GPUs, but the team is actively working on enabling support for additional backends like CPU, AMD ROCm, Intel XPU, and Gaudi HPU.
Table of Contents
CUDA
bitsandbytes is currently supported on NVIDIA GPUs with Compute Capability 5.0+.
The library can be built using CUDA Toolkit versions as old as 11.6 on Windows and 11.4 on Linux.
| Feature | CC Required | Example Hardware Requirement |
|---|---|---|
| LLM.int8() | 7.5+ | Turing (RTX 20 series, T4) or newer GPUs |
| 8-bit optimizers/quantization | 5.0+ | Maxwell (GTX 900 series, TITAN X, M40) or newer GPUs |
| NF4/FP4 quantization | 5.0+ | Maxwell (GTX 900 series, TITAN X, M40) or newer GPUs |
Support for Maxwell GPUs is deprecated and will be removed in a future release. For the best results, a Turing generation device or newer is recommended.
Installation via PyPI
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 | sm50, sm60, sm75, sm80, sm86, sm89, sm90 |
| Linux x86-64 | 12.8 | GCC 11.2 | sm75, sm80, sm86, sm89, sm90, sm100, sm120 |
| Linux aarch64 | 11.8 - 12.6 | GCC 11.2 | sm75, sm80, sm90 |
| Linux aarch64 | 12.8 | GCC 11.2 | sm75, sm80, sm90, sm100 |
| Windows x86-64 | 11.8 - 12.6 | MSVC 19.43+ (VS2022) | sm50, sm60, sm75, sm80, sm86, sm89, sm90 |
| Windows x86-64 | 12.8 | MSVC 19.43+ (VS2022) | sm75, sm80, sm86, sm89, sm90, sm100, sm120 |
Use pip or uv to install:
pip install bitsandbytes
Compile from source
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 >= 3.9 installed. Make sure you have a compiler installed to compile C++ (gcc, make, headers, etc.). It is recommended to use GCC 9 or newer.
For example, to install a compiler and CMake on Ubuntu:
apt-get install -y build-essential cmake
You should also install CUDA Toolkit by following the NVIDIA CUDA Installation Guide for Linux guide. The current minimum supported CUDA Toolkit version that we test with 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)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.
Preview Wheels from main
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.whlMulti-Backend Preview
This functionality existed as an early technical preview and is not recommended for production use. We are in the process of upstreaming improved support for AMD and Intel hardware into the main project.
We provide an early preview of support for AMD and Intel hardware as part of a development branch.
Supported Backends
| Backend | Supported Versions | Python versions | Architecture Support | Status |
|---|---|---|---|---|
| AMD ROCm | 6.1+ | 3.10+ | minimum CDNA - gfx90a, RDNA - gfx1100 | Alpha |
| Intel CPU | v2.4.0+ (ipex) | 3.10+ | Intel CPU | Alpha |
| Intel GPU | v2.4.0+ (ipex) | 3.10+ | Intel GPU | Experimental |
| Ascend NPU | 2.1.0+ (torch_npu) | 3.10+ | Ascend NPU | Experimental |
For each supported backend, follow the respective instructions below:
Pre-requisites
To use this preview version of bitsandbytes with transformers, be sure to install:
pip install "transformers>=4.45.1"Pre-compiled binaries are only built for ROCm versions 6.1.2/6.2.4/6.3.2 and gfx90a, gfx942, gfx1100 GPU architectures. Find the pip install instructions here.
Other supported versions that don’t come with pre-compiled binaries can be compiled for with these instructions.
Windows is not supported for the ROCm backend
If you would like to install ROCm and PyTorch on bare metal, skip the Docker steps and refer to ROCm’s official guides at ROCm installation overview and Installing PyTorch for ROCm (Step 3 of wheels build for quick installation). Special note: please make sure to get the respective ROCm-specific PyTorch wheel for the installed ROCm version, e.g. https://download.pytorch.org/whl/nightly/rocm6.2/!
# Create a docker container with the ROCm image, which includes ROCm libraries
docker pull rocm/dev-ubuntu-22.04:6.3.4-complete
docker run -it --device=/dev/kfd --device=/dev/dri --group-add video rocm/dev-ubuntu-22.04:6.3.4-complete
apt-get update && apt-get install -y git && cd home
# Install pytorch compatible with above ROCm version
pip install torch --index-url https://download.pytorch.org/whl/rocm6.3/Installation
You can install the pre-built wheels for each backend, or compile from source for custom configurations.
Pre-built Wheel Installation (recommended)
This wheel provides support for ROCm and Intel XPU platforms.
# 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_multi-backend-refactor/bitsandbytes-0.44.1.dev0-py3-none-manylinux_2_24_x86_64.whl'Compile from Source
AMD GPU
bitsandbytes is supported from ROCm 6.1 - ROCm 6.4.
# Install bitsandbytes from source
# Clone bitsandbytes repo, ROCm backend is currently enabled on multi-backend-refactor branch
git clone -b multi-backend-refactor 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)