--- title: "bitsandbytes vs OneCompression" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/bitsandbytes-foundation-bitsandbytes-vs-fujitsuresearch-onecompression" tools: ["bitsandbytes-foundation-bitsandbytes", "fujitsuresearch-onecompression"] --- # bitsandbytes vs OneCompression *GraphCanon updated Sep 30, 2026* ## Verdict Pick bitsandbytes if bitsandbytes provides k-bit quantization in PyTorch, enhancing large language model accessibility across multiple hardware platforms; pick OneCompression if oneCompression is a Python package for compressing large language models using techniques such as quantization, with support for CUDA and MPS for different environments. [bitsandbytes](https://huggingface.co/docs/bitsandbytes/main/en/index) reports 8.5k GitHub stars, 938 forks, and 94 open issues, last pushed Sep 7, 2026. [OneCompression](https://fujitsuresearch.github.io/OneCompression/) has 426 stars, 20 forks, and 8 open issues, last pushed Sep 14, 2026. Figures are from public GitHub metadata via [bitsandbytes's repository](https://github.com/bitsandbytes-foundation/bitsandbytes) and [OneCompression's repository](https://github.com/FujitsuResearch/OneCompression). | | [bitsandbytes](/tools/bitsandbytes-foundation-bitsandbytes.md) | [OneCompression](/tools/fujitsuresearch-onecompression.md) | | --- | --- | --- | | Tagline | Large language model quantization toolkit for PyTorch. | Python package for LLM compression | | Stars | 8,507 | 426 | | Forks | 938 | 20 | | Open issues | 94 | 8 | | Language | Python | Python | | Adopt for | bitsandbytes provides k-bit quantization in PyTorch, enhancing large language model accessibility across multiple hardware platforms. | OneCompression is a Python package for compressing large language models using techniques such as quantization, with support for CUDA and MPS for different environments. | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT | | Categories | Inference & Serving, LLM Frameworks | Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [bitsandbytes](/tools/bitsandbytes-foundation-bitsandbytes.md) | [OneCompression](/tools/fujitsuresearch-onecompression.md) | | --- | --- | --- | | Maintenance | Active (82%) | Very active (96%) | | Days since push | 22d | 3d | | Open issues (now) | 94 | 8 | | Stars delta | +122 (30d) | +28 (30d) | | Open issues delta | +40 (30d) | +1 (30d) | | Full report | [trust report](/tools/bitsandbytes-foundation-bitsandbytes/trust.md) | [trust report](/tools/fujitsuresearch-onecompression/trust.md) | ## Shared compatibility - **Python**: [bitsandbytes](/tools/bitsandbytes-foundation-bitsandbytes.md) - Python runtime; [OneCompression](/tools/fujitsuresearch-onecompression.md) - Python runtime ## Decision facts: bitsandbytes - **Adopt for:** bitsandbytes provides k-bit quantization in PyTorch, enhancing large language model accessibility across multiple hardware platforms. ## Decision facts: OneCompression - **Adopt for:** OneCompression is a Python package for compressing large language models using techniques such as quantization, with support for CUDA and MPS for different environments. ## Choose when ### Choose bitsandbytes if… - Tags unique to bitsandbytes: machine-learning, qlora. - When you need advanced k-bit quantization on PyTorch for hardware like NVIDIA GPUs with SM75+ recommended. - More GitHub stars (8.5k vs 426) - visibility, not fit. ### Choose OneCompression if… - Tags unique to OneCompression: compression, cuda, mps, qep. - Also covers Model Training. - Use OneCompression when you need to compress large language models and have access to a Linux system with an NVIDIA GPU, as it supports CUDA for quantization and vLLM serving. ## When NOT to use bitsandbytes - Avoid if your setup includes Intel Gaudi processors as QLoRA 4-bit support is partial and 8-bit optimizers are not available. - Steer clear if you require full compatibility with ARM-based CPUs, as specific GPU optimizations might lack coverage. ## When NOT to use OneCompression - Avoid OneCompression if you are working on a macOS system and require vLLM serving, as it is only supported on Linux with an NVIDIA GPU. - Do not use OneCompression if you are working with Python 3.13 and CUDA, as the first run with `--extra vllm` may take a long time due to the lack of pre-built `xformers` wheels for this combination. - Do not use OneCompression if you need to work with older CUDA versions like `cu118` or `cu121` and require vLLM, as `--extra vllm` is only compatible with `cu130`. ## Common questions ### What is the difference between bitsandbytes and OneCompression? bitsandbytes: Large language model quantization toolkit for PyTorch.. OneCompression: Python package for LLM compression. See the comparison table for live GitHub stats and shared categories. ### When should I choose bitsandbytes over OneCompression? Choose bitsandbytes over OneCompression when Tags unique to bitsandbytes: machine-learning, qlora; When you need advanced k-bit quantization on PyTorch for hardware like NVIDIA GPUs with SM75+ recommended; More GitHub stars (8.5k vs 426) - visibility, not fit. ### When should I choose OneCompression over bitsandbytes? Choose OneCompression over bitsandbytes when Tags unique to OneCompression: compression, cuda, mps, qep; Also covers Model Training; Use OneCompression when you need to compress large language models and have access to a Linux system with an NVIDIA GPU, as it supports CUDA for quantization and vLLM serving. ### When should I avoid bitsandbytes? Avoid if your setup includes Intel Gaudi processors as QLoRA 4-bit support is partial and 8-bit optimizers are not available. Steer clear if you require full compatibility with ARM-based CPUs, as specific GPU optimizations might lack coverage. ### When should I avoid OneCompression? Avoid OneCompression if you are working on a macOS system and require vLLM serving, as it is only supported on Linux with an NVIDIA GPU. Do not use OneCompression if you are working with Python 3.13 and CUDA, as the first run with `--extra vllm` may take a long time due to the lack of pre-built `xformers` wheels for this combination. Do not use OneCompression if you need to work with older CUDA versions like `cu118` or `cu121` and require vLLM, as `--extra vllm` is only compatible with `cu130`. ### Is bitsandbytes or OneCompression more popular on GitHub? bitsandbytes has more GitHub stars (8,507 vs 426). Stars measure visibility, not whether either tool fits your constraints. ### Are bitsandbytes and OneCompression open source? Yes - both are open-source projects on GitHub (bitsandbytes: MIT, OneCompression: MIT). ### Where can I find alternatives to bitsandbytes or OneCompression? GraphCanon lists graph-backed alternatives at [bitsandbytes alternatives](/tools/bitsandbytes-foundation-bitsandbytes/alternatives) and [OneCompression alternatives](/tools/fujitsuresearch-onecompression/alternatives) ([bitsandbytes markdown twin](/tools/bitsandbytes-foundation-bitsandbytes/alternatives.md), [OneCompression markdown twin](/tools/fujitsuresearch-onecompression/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/bitsandbytes-foundation-bitsandbytes-vs-fujitsuresearch-onecompression.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, bitsandbytes or OneCompression? bitsandbytes: Active. OneCompression: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for bitsandbytes and OneCompression? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [bitsandbytes trust report](/tools/bitsandbytes-foundation-bitsandbytes/trust); [OneCompression trust report](/tools/fujitsuresearch-onecompression/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=bitsandbytes-foundation-bitsandbytes`](/api/graphcanon/graph?tool=bitsandbytes-foundation-bitsandbytes) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_