bitsandbytes provides efficient k-bit quantization for large language models in PyTorch, enabling accessible deployment on consumer hardware. It targets researchers and developers working with LLMs who need to reduce memory footprint and improve inference speed.

How It Works

The library wraps custom CUDA functions for 8-bit optimizers, matrix multiplication (LLM.int8()), and 8- & 4-bit quantization. It offers bitsandbytes.nn.Linear8bitLt and bitsandbytes.nn.Linear4bit for quantization-aware layers and bitsandbytes.optim for 8-bit optimizers, reducing memory usage and potentially speeding up computations.

Quick Start & Requirements

• Install via pip: pip install bitsandbytes
• Requires PyTorch and CUDA-enabled GPU.
• Official documentation: https://huggingface.co/docs/bitsandbytes/main

Highlighted Details

• Enables 8-bit and 4-bit quantization for LLMs.
• Includes 8-bit optimizers.
• Supports LLM.int8() matrix multiplication.

Maintenance & Community

• Ongoing efforts to support Intel CPU+GPU, AMD GPU, Apple Silicon, and NPUs.
• Official documentation hosted on Hugging Face.

Licensing & Compatibility

• MIT licensed.
• Compatible with commercial and closed-source applications.

Limitations & Caveats

The library primarily targets NVIDIA GPUs with CUDA. Support for other hardware backends is under development and may not be production-ready.