# Installation

## CUDA

bitsandbytes is only supported on CUDA GPUs for CUDA versions **11.0 - 12.5**. However, there's a multi-backend effort under way which is currently in alpha release, check [the respective section below in case you're interested to help us with early feedback](#multi-backend).

The latest version of bitsandbytes builds on:

| OS | CUDA | Compiler |
|---|---|---|
| Linux | 11.7 - 12.3 | GCC 11.4 |
|  | 12.4+ | GCC 13.2 |
| Windows | 11.7 - 12.4 | MSVC 19.38+ (VS2022 17.8.0+) |

> [!TIP]
> MacOS support is still a work in progress! Subscribe to this [issue](https://github.com/TimDettmers/bitsandbytes/issues/1020) to get notified about discussions and to track the integration progress.

For Linux systems, make sure your hardware meets the following requirements to use bitsandbytes features.

| **Feature** | **Hardware requirement** |
|---|---|
| LLM.int8() | NVIDIA Turing (RTX 20 series, T4) or Ampere (RTX 30 series, A4-A100) GPUs |
| 8-bit optimizers/quantization | NVIDIA Kepler (GTX 780 or newer) |

> [!WARNING]
> bitsandbytes >= 0.39.1 no longer includes Kepler binaries in pip installations. This requires manual compilation, and you should follow the general steps and use `cuda11x_nomatmul_kepler` for Kepler-targeted compilation.

To install from PyPI.

```bash
pip install bitsandbytes
```

### Compile from source[[compile]]

For Linux and Windows systems, you can compile bitsandbytes from source. Installing from source allows for more build options with different CMake configurations.

<hfoptions id="source">
<hfoption id="Linux">

To compile from source, you need CMake >= **3.22.1** and Python >= **3.8** installed. Make sure you have a compiler installed to compile C++ (gcc, make, headers, etc.). For example, to install a compiler and CMake on Ubuntu:

```bash
apt-get install -y build-essential cmake
```

You should also install CUDA Toolkit by following the [NVIDIA CUDA Installation Guide for Linux](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html) guide from NVIDIA. The current expected CUDA Toolkit version is **11.1+** and it is recommended to install **GCC >= 7.3** and required to have at least **GCC >= 6**.

Refer to the following table if you're using another CUDA Toolkit version.

| CUDA Toolkit | GCC |
|---|---|
| >= 11.4.1 | >= 11 |
| >= 12.0 | >= 12 |
| >= 12.4 | >= 13 |

Now to install the bitsandbytes package from source, run the following commands:

```bash
git clone https://github.com/TimDettmers/bitsandbytes.git && cd bitsandbytes/
pip install -r requirements-dev.txt
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 CUDA installed or installed it in a non-standard location, please refer to CMake CUDA documentation for how to configure the CUDA compiler.

</hfoption>
<hfoption id="Windows">

Windows systems require Visual Studio with C++ support as well as an installation of the CUDA SDK.

To compile from source, you need CMake >= **3.22.1** and Python >= **3.8** installed. You should also install CUDA Toolkit by following the [CUDA Installation Guide for Windows](https://docs.nvidia.com/cuda/cuda-installation-guide-microsoft-windows/index.html) guide from NVIDIA.

Refer to the following table if you're using another CUDA Toolkit version.

| CUDA Toolkit | MSVC |
|---|---|
| >= 11.6 | 19.30+ (VS2022) |

```bash
git clone https://github.com/TimDettmers/bitsandbytes.git && cd bitsandbytes/
pip install -r requirements-dev.txt
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](https://github.com/wkpark), [Jamezo97](https://github.com/Jamezo97), [rickardp](https://github.com/rickardp), [akx](https://github.com/akx) for their amazing contributions to make bitsandbytes compatible with Windows.

</hfoption>
</hfoptions>

### PyTorch CUDA versions

Some bitsandbytes features may need a newer CUDA version than the one currently supported by PyTorch binaries from Conda and pip. In this case, you should follow these instructions to load a precompiled bitsandbytes binary.

1. Determine the path of the CUDA version you want to use. Common paths include:

* `/usr/local/cuda`
* `/usr/local/cuda-XX.X` where `XX.X` is the CUDA version number

Then locally install the CUDA version you need with this script from bitsandbytes:

```bash
wget https://raw.githubusercontent.com/TimDettmers/bitsandbytes/main/install_cuda.sh
# Syntax cuda_install CUDA_VERSION INSTALL_PREFIX EXPORT_TO_BASH
#   CUDA_VERSION in {110, 111, 112, 113, 114, 115, 116, 117, 118, 120, 121, 122, 123, 124, 125}
#   EXPORT_TO_BASH in {0, 1} with 0=False and 1=True

# For example, the following installs CUDA 11.7 to ~/local/cuda-11.7 and exports the path to your .bashrc

bash install_cuda.sh 117 ~/local 1
```

2. Set the environment variables `BNB_CUDA_VERSION` and `LD_LIBRARY_PATH` by manually overriding the CUDA version installed by PyTorch.

> [!TIP]
> It is recommended to add the following lines to the `.bashrc` file to make them permanent.

```bash
export BNB_CUDA_VERSION=<VERSION>
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:<PATH>
```

For example, to use a local install path:

```bash
export BNB_CUDA_VERSION=117
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/home/YOUR_USERNAME/local/cuda-11.7
```

3. Now when you launch bitsandbytes with these environment variables, the PyTorch CUDA version is overridden by the new CUDA version (in this example, version 11.7) and a different bitsandbytes library is loaded.

## Multi-backend preview release compilation[[multi-backend]]

Please follow these steps to install bitsandbytes with device-specific backend support other than CUDA:

<hfoptions id="backend">
<hfoption id="AMD ROCm">

### AMD GPU

bitsandbytes is fully supported from ROCm 6.1 onwards (currently in alpha release).

> [!TIP]
> If you already installed ROCm and PyTorch, skip Docker steps below and please check that the torch version matches your ROCm install. To install torch for a specific ROCm version, please refer to step 3 of wheels install in [Installing PyTorch for ROCm](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/how-to/3rd-party/pytorch-install.html#using-wheels-package) guide.

```bash
# Create a docker container with latest pytorch. It comes with ROCm and pytorch preinstalled
docker pull rocm/pytorch:latest
docker run -it --device=/dev/kfd --device=/dev/dri --group-add video rocm/pytorch:latest

# Clone bitsandbytes repo, ROCm backend is currently enabled on multi-backend-refactor branch
git clone --depth 1 -b multi-backend-refactor https://github.com/TimDettmers/bitsandbytes.git && cd bitsandbytes/

# Install dependencies
pip install -r requirements-dev.txt

# 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)
```

</hfoption>
<hfoption id="Intel CPU + GPU">

### Intel CPU

> [!TIP]
> Intel CPU backend only supports building from source; for now, please follow the instructions below.

Similar to the CUDA case, you can compile bitsandbytes from source for Linux and Windows systems.

The below commands are for Linux. For installing on Windows, please adapt the below commands according to the same pattern as described [the section above on compiling from source under the Windows tab](#compile).

```
git clone --depth 1 -b multi-backend-refactor https://github.com/TimDettmers/bitsandbytes.git && cd bitsandbytes/
pip install intel_extension_for_pytorch
pip install -r requirements-dev.txt
cmake -DCOMPUTE_BACKEND=cpu -S .
make
pip install -e .   # `-e` for "editable" install, when developing BNB (otherwise leave that out)
```

</hfoption>
<hfoption id="Apple Silicon (MPS)">

WIP

</hfoption>
</hfoptions>