--- title: "OneCompression vs headroom" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/fujitsuresearch-onecompression-vs-headroomlabs-ai-headroom" tools: ["fujitsuresearch-onecompression", "headroomlabs-ai-headroom"] --- # OneCompression vs headroom *GraphCanon updated Sep 20, 2026* ## Verdict 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; pick headroom if headroom compresses data for LLMs, reducing token usage by 20% for coding agents and 60-95% for JSON, without altering answers. It offers a library, proxy, and MCP server. [OneCompression](https://fujitsuresearch.github.io/OneCompression/) reports 426 GitHub stars, 20 forks, and 8 open issues, last pushed Sep 14, 2026. [headroom](https://docs.headroomlabs.ai/docs) has 73k stars, 5.6k forks, and 671 open issues, last pushed Sep 17, 2026. Figures are from public GitHub metadata via [OneCompression's repository](https://github.com/FujitsuResearch/OneCompression) and [headroom's repository](https://github.com/headroomlabs-ai/headroom). | | [OneCompression](/tools/fujitsuresearch-onecompression.md) | [headroom](/tools/headroomlabs-ai-headroom.md) | | --- | --- | --- | | Tagline | Python package for LLM compression | Compress tool outputs, logs, files, and RAG chunks before they reach the LLM. | | Stars | 426 | 72,850 | | Forks | 20 | 5,600 | | Open issues | 8 | 671 | | Language | Python | Python | | 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. | Headroom compresses data for LLMs, reducing token usage by 20% for coding agents and 60-95% for JSON, without altering answers. It offers a library, proxy, and MCP server. | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | Inference & Serving, LLM Frameworks, Model Training | Developer Tools, Evaluation & Observability, Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [OneCompression](/tools/fujitsuresearch-onecompression.md) | [headroom](/tools/headroomlabs-ai-headroom.md) | | --- | --- | --- | | Days since push | 3d | 0d | | Open issues (now) | 8 | 671 | | Stars delta | +28 (30d) | +6.4k (30d) | | Open issues delta | +1 (30d) | +183 (30d) | | Full report | [trust report](/tools/fujitsuresearch-onecompression/trust.md) | [trust report](/tools/headroomlabs-ai-headroom/trust.md) | ## Shared compatibility - **Python**: [OneCompression](/tools/fujitsuresearch-onecompression.md) - Python runtime; [headroom](/tools/headroomlabs-ai-headroom.md) - Python runtime ## 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. ## Decision facts: headroom - **Requirements:** Requires Docker; Requires Python 3.10+.; ONNX-backed features require AVX2 on x86/x86_64 hosts. - **Adopt for:** Headroom compresses data for LLMs, reducing token usage by 20% for coding agents and 60-95% for JSON, without altering answers. It offers a library, proxy, and MCP server. ## Choose when ### Choose OneCompression if… - License: OneCompression is MIT, headroom is Apache-2.0. - Tags unique to OneCompression: cuda, llm, mps, pytorch. - Also covers LLM Frameworks. - 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. ### Choose headroom if… - License: headroom is Apache-2.0, OneCompression is MIT. - Requirements: Requires Docker; Requires Python 3.10+.; ONNX-backed features require AVX2 on x86/x86_64 hosts.. - Tags unique to headroom: agent, ai, anthropic, claude-code. - Also covers Developer Tools, Evaluation & Observability. - headroom ships Docker support for self-hosted deployment. - When you need to reduce token usage for coding agents by 20% and for JSON by 60-95% without changing the answers. ## 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`. ## When NOT to use headroom - If you are working with environments that do not support Python 3.10+. - When your project does not require token optimization or compression for JSON and coding agents. - If you are working on a platform that does not support the ONNX-backed features, such as some Docker/QEMU setups or older cloud VMs without AVX2. ## Common questions ### What is the difference between OneCompression and headroom? OneCompression: Python package for LLM compression. headroom: Compress tool outputs, logs, files, and RAG chunks before they reach the LLM.. See the comparison table for live GitHub stats and shared categories. ### When should I choose OneCompression over headroom? Choose OneCompression over headroom when License: OneCompression is MIT, headroom is Apache-2.0; Tags unique to OneCompression: cuda, llm, mps, pytorch; Also covers LLM Frameworks; 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 choose headroom over OneCompression? Choose headroom over OneCompression when License: headroom is Apache-2.0, OneCompression is MIT; Requirements: Requires Docker; Requires Python 3.10+.; ONNX-backed features require AVX2 on x86/x86_64 hosts.; Tags unique to headroom: agent, ai, anthropic, claude-code; Also covers Developer Tools, Evaluation & Observability; headroom ships Docker support for self-hosted deployment; When you need to reduce token usage for coding agents by 20% and for JSON by 60-95% without changing the answers. ### 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`. ### When should I avoid headroom? If you are working with environments that do not support Python 3.10+. When your project does not require token optimization or compression for JSON and coding agents. If you are working on a platform that does not support the ONNX-backed features, such as some Docker/QEMU setups or older cloud VMs without AVX2. ### Is OneCompression or headroom more popular on GitHub? headroom has more GitHub stars (72,850 vs 426). Stars measure visibility, not whether either tool fits your constraints. ### Are OneCompression and headroom open source? Yes - both are open-source projects on GitHub (OneCompression: MIT, headroom: Apache-2.0). ### Where can I find alternatives to OneCompression or headroom? GraphCanon lists graph-backed alternatives at [OneCompression alternatives](/tools/fujitsuresearch-onecompression/alternatives) and [headroom alternatives](/tools/headroomlabs-ai-headroom/alternatives) ([OneCompression markdown twin](/tools/fujitsuresearch-onecompression/alternatives.md), [headroom markdown twin](/tools/headroomlabs-ai-headroom/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/fujitsuresearch-onecompression-vs-headroomlabs-ai-headroom.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, OneCompression or headroom? OneCompression: Very active. headroom: 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 OneCompression and headroom? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [OneCompression trust report](/tools/fujitsuresearch-onecompression/trust); [headroom trust report](/tools/headroomlabs-ai-headroom/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=fujitsuresearch-onecompression`](/api/graphcanon/graph?tool=fujitsuresearch-onecompression) - 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/_