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docs update
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matthewdouglas committed Nov 29, 2024
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2 changes: 1 addition & 1 deletion docs/source/algorithms.mdx
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Expand Up @@ -5,7 +5,7 @@ This is an overview of the `bnb.functional` API in `bitsandbytes` that we think

## Using Int8 Matrix Multiplication

For straight Int8 matrix multiplication with mixed precision decomposition you can use ``bnb.matmul(...)``. To enable mixed precision decomposition, use the threshold parameter:
For straight Int8 matrix multiplication without mixed precision decomposition you can use ``bnb.matmul(...)``. To enable mixed precision decomposition, use the threshold parameter:

```py
bnb.matmul(..., threshold=6.0)
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31 changes: 16 additions & 15 deletions docs/source/installation.mdx
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Expand Up @@ -19,29 +19,30 @@ Welcome to the installation guide for the `bitsandbytes` library! This document

## CUDA[[cuda]]

`bitsandbytes` is currently only supported on CUDA GPUs for CUDA versions **11.0 - 12.5**. However, there's an ongoing multi-backend effort under development, which is currently in alpha. If you're interested in providing feedback or testing, check out [the multi-backend section below](#multi-backend).
`bitsandbytes` is currently only supported on CUDA GPUs for CUDA versions **11.0 - 12.6**. However, there's an ongoing multi-backend effort under development, which is currently in alpha. If you're interested in providing feedback or testing, check out [the multi-backend section below](#multi-backend).

### Supported CUDA Configurations[[cuda-pip]]

The latest version of `bitsandbytes` builds on the following configurations:
The latest version of the distributed `bitsandbytes` package is built with the following configurations:

| **OS** | **CUDA Version** | **Compiler** |
| **OS** | **CUDA Toolkit** | **Host Compiler** |
|-------------|------------------|----------------------|
| **Linux** | 11.7 - 12.3 | GCC 11.4 |
| | 12.4+ | GCC 13.2 |
| **Windows** | 11.7 - 12.4 | MSVC 19.38+ (VS2022) |
| | 12.4 - 12.6 | GCC 13.2 |
| **Windows** | 11.7 - 12.6 | MSVC 19.42+ (VS2022) |

For Linux systems, ensure your hardware meets the following requirements:
For CUDA systems, ensure your hardware meets the following requirements:

| **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) |
| **Feature** | **Minimum Hardware Requirement** |
|---------------------------------|---------------------------------------------------------------|
| LLM.int8() | NVIDIA Turing (RTX 20 series, T4) or newer GPUs |
| 8-bit optimizers/quantization | NVIDIA Maxwell (GTX 900 series, TITAN X, M40) or newer GPUs * |
| NF4/FP4 quantization | NVIDIA Maxwell (GTX 900 series, TITAN X, M40) or newer GPUs * |

> [!WARNING]
> `bitsandbytes >= 0.39.1` no longer includes Kepler binaries in pip installations. This requires [manual compilation using](#cuda-compile) the `cuda11x_nomatmul_kepler` configuration.
To install from PyPI.
> `bitsandbytes >= 0.45.0` no longer supports Kepler 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.
```bash
pip install bitsandbytes
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<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.).
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.).

For example, to install a compiler and CMake on Ubuntu:

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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.
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](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.

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