#
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.

We provide official support for NVIDIA GPUs, CPUs, Intel XPUs, and Intel Gaudi. We also have experimental support for additional platforms such as AMD ROCm and Apple Silicon.

##
Table of Contents

- System Requirements
- NVIDIA CUDA
  - Installation via PyPI
  - Compile from Source
- Intel XPU
  - Installation via PyPI
- Intel Gaudi
  - Installation via PyPI
- CPU
  - Installation via PyPI
  - Compile from Source
- AMD ROCm (Preview)
  - Installation via PyPI
  - Compile from Source
- Preview Wheels

##
System Requirements

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

##
NVIDIA CUDA

`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

###
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 | 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, 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, sm110, sm120, sm121
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 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):

Linux
Windows

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 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 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)

> 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.

##
Intel XPU

- 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.

###
Installation via PyPI

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

##
Intel Gaudi

- 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.

###
Installation from PyPI

Use `pip` or `uv` to install the latest release:

    pip install bitsandbytes

##
CPU

###
Installation from PyPI

This 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
macOS arm64 | Apple Clang 17 |

The Linux build has a minimum glibc version of 2.24.

Use `pip` or `uv` to install the latest release:

    pip install bitsandbytes

###
Compile from Source

To 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 .

##
AMD ROCm (Preview)

- Support for AMD GPUs is currently in a preview state.
- All features are supported for consumer RDNA devices.
- The Data Center products currently lack support for the default 4bit blocksize of 64, and as such default to 128.
- 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.

###
Installation from PyPI

This is the most straightforward and recommended installation option.

The currently distributed `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, gfx1150, gfx1151, gfx1200, gfx1201
Linux x86-64 | 7.0.2 | CDNA: gfx90a, gfx942, gfx950 / RDNA: gfx1100, gfx1101, gfx1150, gfx1151, gfx1200, gfx1201
Linux x86-64 | 7.1.0 | CDNA: gfx90a, gfx942, gfx950 / RDNA: gfx1100, gfx1101, gfx1150, gfx1151, gfx1200, gfx1201

Windows is not currently supported.

Use `pip` or `uv` to install the latest release:

    pip install bitsandbytes

###
Compile from Source

bitsandbytes can be compiled from ROCm 6.2 - ROCm 7.1.

To compile from source, you need CMake >= 3.31.6.

    # 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)

##
Preview Wheels

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!):

Linux
Windows
macOS

    # 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

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