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Snapshot 0 · 2026-11-04T12:47:26Z · Python · MIT
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68fc2df60317162ca34ba4a1aa0ff3dbc195634391ddeff326b3b109ff7d5ea3GitHub description at capture
Accessible large language models via k-bit quantization for PyTorch.
README at capture
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<h1 align="center">bitsandbytes</h1>
<p align="center">
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</p>
`bitsandbytes` enables accessible large language models via k-bit quantization for PyTorch. We provide three main features for dramatically reducing memory consumption for inference and training:
* 8-bit optimizers uses block-wise quantization to maintain 32-bit performance at a small fraction of the memory cost.
* LLM.int8() or 8-bit quantization enables large language model inference with only half the required memory and without any performance degradation. This method is based on vector-wise quantization to quantize most features to 8-bits and separately treating outliers with 16-bit matrix multiplication.
* QLoRA or 4-bit quantization enables large language model training with several memory-saving techniques that don't compromise performance. This method quantizes a model to 4-bits and inserts a small set of trainable low-rank adaptation (LoRA) weights to allow training.
The library includes quantization primitives for 8-bit & 4-bit operations, through `bitsandbytes.nn.Linear8bitLt` and `bitsandbytes.nn.Linear4bit` and 8-bit optimizers through `bitsandbytes.optim` module.
## System Requirements
bitsandbytes has the following minimum requirements for all platforms:
* Python 3.10+
* [PyTorch](https://pytorch.org/get-started/locally/) 2.4+
* _Note: While we aim to provide wide backwards compatibility, we recommend using the latest version of PyTorch for the best experience._
#### Accelerator support:
<small>Note: this table reflects the status of the current development branch. For the latest stable release, see the
[document in the 0.50.0 tag](https://github.com/bitsandbytes-foundation/bitsandbytes/blob/0.50.0/README.md#accelerator-support).
</small>
##### Legend:
🚧 = Planned |
〰️ = Partially Supported |
✅ = Supported |
❌ = Not Supported
<table>
<thead>
<tr>
<th>Platform</th>
<th>Accelerator</th>
<th>Hardware Requirements</th>
<th>LLM.int8()</th>
<th>QLoRA 4-bit</th>
<th>8-bit Optimizers</th>
</tr>
</thead>
<tbody>
<tr>
<td colspan="6">🐧 <strong>Linux, glibc >= 2.24</strong></td>
</tr>
<tr>
<td align="right">x86-64</td>
<td>◻️ CPU</td>
<td>Minimum: AVX2<br>Optimized: AVX512F, AVX512BF16</td>
<td>✅</td>
<td>✅</td>
<td>✅</td>
</tr>
<tr>
<td></td>
<td>🟩 NVIDIA GPU <br><code>cuda</code></td>
<td>SM60+ minimum<br>SM75+ recommended</td>
<td>✅</td>
<td>✅</td>
<td>✅</td>
</tr>
<tr>
<td></td>
<td>🟥 AMD GPU <br><code>cuda</code></td>
<td>
CDNA: gfx908, gfx90a, gfx942, gfx950, gfx1250<br>
RDNA: gfx101X, gfx103X, gfx110X, gfx115X, gfx120X
</td>
<td>✅</td>
<td>✅</td>
<td>✅</td>
</tr>
<tr>
<td></td>
<td>🟦 Intel GPU <br><code>xpu</code></td>
<td>
Data Center GPU Max Series<br>
Arc A-Series (Alchemist)<br>
Arc B-Series (Battlemage)
</td>
<td>✅</td>
<td>✅</td>
<td>✅</td>
</tr>
<tr>
<td></td>
<td>🟪 Intel Gaudi <br><code>hpu</code></td>
<td>Gaudi2, Gaudi3</td>
<td>✅</td>
<td>〰️</td>
<td>❌</td>
</tr>
<tr>
<td align="right">aarch64</td>
<td>◻️ CPU</td>
<td></td>
<td>✅ *</td>
<td>✅</td>
<td>✅</td>
</tr>
<tr>
<td></td>
<td>🟩 NVIDIA GPU <br><code>cuda</code></td>
<td>SM75+</td>
<td>✅</td>
<td>✅</td>
<td>✅</td>
</tr>
<tr>
<td colspan="6">🪟 <strong>Windows 11 / Windows Server 2022+</strong></td>
</tr>
<tr>
<td align="right">x86-64</td>
<td>◻️ CPU</td>
<td>AVX2</td>
<td>✅</td>
<td>✅</td>
<td>✅</td>
</tr>
<tr>
<td></td>
<td>🟩 NVIDIA GPU <br><code>cuda</code></td>
<td>SM60+ minimum<br>SM75+ recommended</td>
<td>✅</td>
<td>✅</td>
<td>✅</td>
</tr>
<tr>
<td></td>
<td>🟥 AMD GPU <br><code>cuda</code></td>
<td>
CDNA: gfx908, gfx90a<br>
RDNA: gfx101X, gfx103X, gfx110X, gfx115X, gfx120X
</td>
<td>✅</td>
<td>✅</td>
<td>✅</td>
</tr>
<tr>
<td></td>
<td>🟦 Intel GPU <br><code>xpu</code></td>
<td>
Arc A-Series (Alchemist) <br>
Arc B-Series (Battlemage)
</td>
<td>✅</td>
<td>✅</td>
<td>✅</td>
</tr>
<tr>
<td align="right">arm64</td>
<td>◻️ CPU</td>
<td></td>
<td>✅</td>
<td>✅</td>
<td>✅</td>
</tr>
<tr>
<td></td>
<td>🟩 NVIDIA GPU <br><code>cuda</code></td>
<td>SM121</td>
<td>✅</td>
<td>✅</td>
<td>✅</td>
</tr>
<tr>
<td colspan="6">🍎 <strong>macOS 14+</strong></td>
</tr>
<tr>
<td align="right">arm64</td>
<td>◻️ CPU</td>
<td>Apple M1+</td>
<td>✅ *</td>
<td>✅</td>
<td>✅</td>
</tr>
<tr>
<td></td>
<td>⬜ Metal <br><code>mps</code></td>
<td>Apple M1+</td>
<td>✅ *</td>
<td>✅</td>
<td>🚧</td>
</tbody>
</table>
<sup>* While supported, these marked features may lack in performance optimizations.</sup>
## :book: Documentation
* [Official Documentation](https://huggingface.co/docs/bitsandbytes/main)
* 🤗 [Transformers](https://huggingface.co/docs/transformers/quantization/bitsandbytes)
* 🤗 [Diffusers](https://huggingface.co/docs/diffusers/quantization/bitsandbytes)
* 🤗 [PEFT](https://huggingface.co/docs/peft/developer_guides/quantization#quantize-a-model)
## :heart: Sponsors
The continued maintenance and development of `bitsandbytes` is made possible thanks to the generous support of our sponsors. Their contributions help ensure that we can keep improving the project and delivering valuable updates to the community.
<kbd><a href="https://hf.co" target="_blank"><img width="100" src="https://huggingface.co/datasets/huggingface/brand-assets/resolve/main/hf-logo.svg" alt="Hugging Face"></a></kbd>
## License
`bitsandbytes` is MIT licensed.
## How to cite us
If you found this library useful, please consider citing our work:
### QLoRA
```bibtex
@article{dettmers2023qlora,
title={Qlora: Efficient finetuning of quantized llms},
author={Dettmers, Tim and Pagnoni, Artidoro and Holtzman, Ari and Zettlemoyer, Luke},
journal={arXiv preprint arXiv:2305.14314},
year={2023}
}
```
### LLM.int8()
```bibtex
@article{dettmers2022llmint8,
title={LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale},
author={Dettmers, Tim and Lewis, Mike and Belkada, Younes and Zettlemoyer, Luke},
journal={arXiv preprint arXiv:2208.07339},
year={2022}
}
```
### 8-bit Optimizers
```bibtex
@article{dettmers2022optimizers,
title={8-bit Optimizers via Block-wise Quantization},
author={Dettmers, Tim and Lewis, Mike and Shleifer, Sam and Zettlemoyer, Luke},
journal={9th International Conference on Learning Representations, ICLR},
year={2022}
}
```