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Bitsandbytes

Bitsandbytes is an open-source Python library for PyTorch that provides 8-bit and 4-bit large-language-model quantization, memory-efficient 8-bit optimizers, and QLoRA building blocks for reducing memory use during inference and training.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Bitsandbytes — OpenViST project profile in AI Infrastructure & Serving.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Bitsandbytes when a PyTorch language-model workflow needs lower inference, training or optimizer memory consumption and its Python, PyTorch and hardware requirements are satisfied.

Who it is for

  • PyTorch developers building memory-constrained LLM inference or serving workflows.
  • Teams preparing low-memory fine-tuning pipelines with QLoRA.
  • Researchers and engineers evaluating 8-bit optimizer workflows for model training.
  • Builders creating self-hosted applications around quantized language models.

Documented uses

  • Reduce the memory footprint of large-language-model inference with LLM.int8() or other 8-bit quantization.
  • Prepare 4-bit QLoRA workflows for memory-efficient model fine-tuning.
  • Use block-wise 8-bit optimizers to reduce optimizer-state memory during PyTorch training.
  • Add quantization primitives such as Linear8bitLt or Linear4bit to a PyTorch model workflow.
Before you choose

Requirements & limits

The project is publicly available as an open-source MIT-licensed Python library. Its documented baseline is Python 3.10 or newer and PyTorch 2.4 or newer; platform and accelerator support varies by feature.

  • LLM.int8() and 8-bit quantization are documented for inference with half the required memory.
  • QLoRA uses 4-bit model quantization with trainable low-rank adaptation weights.
  • The library exposes separate 8-bit and 4-bit linear-layer primitives and an optimizer module.
  • Feature support differs across Linux, Windows and macOS, CPU types, GPUs and other accelerators.
  • The current support matrix notes that some supported CPU paths may lack performance optimizations.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Prepare a PyTorch project for Bitsandbytes quantization

Set up the documented Python and PyTorch foundation for using Bitsandbytes quantization primitives.

Before you start

  • Python 3.10 or newer.
  • PyTorch 2.4 or newer.
  • A supported Linux, Windows or macOS configuration and accelerator path for the intended feature.

Steps

  1. Use a Python environment meeting the stated minimum version.

  2. Provide PyTorch 2.4 or newer.

  3. Choose the documented Bitsandbytes feature and check its operating-system, processor and accelerator entry in the support matrix.

  4. Integrate the relevant quantization primitive or optimizer module into the PyTorch project.

Expected outcome

A PyTorch project is prepared to use the library’s documented 8-bit or 4-bit quantization and optimizer components.

Guide limitations

  • Support and performance vary by accelerator and platform.
  • The documentation notes that some supported CPU paths may lack performance optimizations.
  • The support table describes the development branch; the stable-release matrix may differ.
Documentation 1 ↗Documentation 2 ↗
COMPARE OPTIONS

Compare options for Model inference and serving

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
BitsandbytesReduce the memory footprint of large-language-model inference with LLM.int8() or other 8-bit quantization.Linux, macOS, Self-hosted, ServerThe project is publicly available as an open-source MIT-licensed Python library. Its documented baseline is Python 3.10 or newer and PyTorch 2.4 or newer; platform and accelerator support varies by feature.
Cloud AI SDKAdapt and run documented model recipes for computer-vision and NLP inference.Cloud, ServerThe GitHub repository is public. Its public visibility does not establish license terms.
TurboquantReplace a model’s existing inference cache with a compressed cache.Cloud, Linux, macOS, Self-hostedThe project describes itself as open source. The README documents installation through pip or from a source checkout.
Evidence and transparency

Sources & review

Official repository ↗Guide documentation ↗
Source record details
Source availability
Active
Sources checked
2 October 2026
Original website title
bitsandbytes-foundation/bitsandbytes · Official homepage title · retained in its source language
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