--- title: "OneCompression vs litgpt" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/fujitsuresearch-onecompression-vs-lightning-ai-litgpt" tools: ["fujitsuresearch-onecompression", "lightning-ai-litgpt"] --- # OneCompression vs litgpt *GraphCanon updated Jul 12, 2026* ## Verdict Pick OneCompression when license: OneCompression is MIT, litgpt is Apache-2.0; pick litgpt when license: litgpt is Apache-2.0, OneCompression is MIT. [OneCompression](https://fujitsuresearch.github.io/OneCompression/) reports 396 GitHub stars, 18 forks, and 6 open issues, last pushed Jul 6, 2026. [litgpt](https://lightning.ai) has 13k stars, 1.5k forks, and 267 open issues, last pushed Jul 6, 2026. Figures are from public GitHub metadata via [OneCompression's repository](https://github.com/FujitsuResearch/OneCompression) and [litgpt's repository](https://github.com/Lightning-AI/litgpt). | | [OneCompression](/tools/fujitsuresearch-onecompression.md) | [litgpt](/tools/lightning-ai-litgpt.md) | | --- | --- | --- | | Tagline | Python package for LLM compression | High-performance LLMs with recipes for pretraining, finetuning and deployment | | Stars | 396 | 13,473 | | Forks | 18 | 1,468 | | Open issues | 6 | 267 | | Language | Python | Python | | Adopt for | - | LitGPT offers extensive support for high-performance LLMs with comprehensive workflows for pretraining, fine-tuning, and deployment. | | Persona | - | - | | Runtime | - | - | | License | MIT | LitGPT operates under the open-source Apache-2.0 license, providing permissive terms for use and modification. | | Categories | LLM Frameworks, Model Training, Inference & Serving | LLM Frameworks, Model Training, Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [OneCompression](/tools/fujitsuresearch-onecompression.md) | [litgpt](/tools/lightning-ai-litgpt.md) | | --- | --- | --- | | Days since push | 5d | 4d | | Open issues (now) | 6 | 267 | | Full report | [trust report](/tools/fujitsuresearch-onecompression/trust.md) | [trust report](/tools/lightning-ai-litgpt/trust.md) | ## Shared compatibility - **Python**: [OneCompression](/tools/fujitsuresearch-onecompression.md) - Python runtime; [litgpt](/tools/lightning-ai-litgpt.md) - Python runtime ## Decision facts: litgpt - **Pricing:** freemium - The core LitGPT framework is free to use under an open source license, but users might encounter costs when deploying at scale or using high-performance models. - **Requirements:** Min 16 GB RAM - **Adopt for:** LitGPT offers extensive support for high-performance LLMs with comprehensive workflows for pretraining, fine-tuning, and deployment. - **License detail:** LitGPT operates under the open-source Apache-2.0 license, providing permissive terms for use and modification. ## Choose when ### Choose OneCompression if… - License: OneCompression is MIT, litgpt is Apache-2.0. - Tags unique to OneCompression: qep, llm, vllm, python. - Leaner open-issue backlog (6). ### Choose litgpt if… - License: litgpt is Apache-2.0, OneCompression is MIT. - Pricing: The core LitGPT framework is free to use under an open source license, but users might encounter costs when deploying at scale or using high-performance models.. - Requirements: Min 16 GB RAM. - Tags unique to litgpt: llms, deep-learning, ai, artificial-intelligence. - If you are focusing on a project that requires rapid prototyping or experimentation with over 20 different LLMs to find the best fit for your application. ## When NOT to use OneCompression - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. - Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. ## When NOT to use litgpt - If you need a tool specifically optimized for resource-constrained devices, as LitGPT focuses on high-performance LLMs and may require more resources. - When your project is strictly limited to only one or two types of specific LLMs; in this case, another specialized framework that caters narrowly might be preferable. ## Common questions ### What is the difference between OneCompression and litgpt? OneCompression: Python package for LLM compression. litgpt: High-performance LLMs with recipes for pretraining, finetuning and deployment. See the comparison table for live GitHub stats and shared categories. ### When should I choose OneCompression over litgpt? Choose OneCompression over litgpt when License: OneCompression is MIT, litgpt is Apache-2.0; Tags unique to OneCompression: qep, llm, vllm, python; Leaner open-issue backlog (6). ### When should I choose litgpt over OneCompression? Choose litgpt over OneCompression when License: litgpt is Apache-2.0, OneCompression is MIT; Pricing: The core LitGPT framework is free to use under an open source license, but users might encounter costs when deploying at scale or using high-performance models.; Requirements: Min 16 GB RAM; Tags unique to litgpt: llms, deep-learning, ai, artificial-intelligence; If you are focusing on a project that requires rapid prototyping or experimentation with over 20 different LLMs to find the best fit for your application. ### When should I avoid OneCompression? LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. ### When should I avoid litgpt? If you need a tool specifically optimized for resource-constrained devices, as LitGPT focuses on high-performance LLMs and may require more resources. When your project is strictly limited to only one or two types of specific LLMs; in this case, another specialized framework that caters narrowly might be preferable. ### Is OneCompression or litgpt more popular on GitHub? litgpt has more GitHub stars (13,473 vs 396). Stars measure visibility, not whether either tool fits your constraints. ### Are OneCompression and litgpt open source? Yes - both are open-source projects on GitHub (OneCompression: MIT, litgpt: Apache-2.0). ### Where can I find alternatives to OneCompression or litgpt? GraphCanon lists graph-backed alternatives at [OneCompression alternatives](/tools/fujitsuresearch-onecompression/alternatives) and [litgpt alternatives](/tools/lightning-ai-litgpt/alternatives) ([OneCompression markdown twin](/tools/fujitsuresearch-onecompression/alternatives.md), [litgpt markdown twin](/tools/lightning-ai-litgpt/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-lightning-ai-litgpt.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, OneCompression or litgpt? OneCompression: Very active. litgpt: 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 litgpt? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [OneCompression trust report](/tools/fujitsuresearch-onecompression/trust); [litgpt trust report](/tools/lightning-ai-litgpt/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/_