Comparison
OneCompression vs litgpt
Verdict
Pick OneCompression if oneCompression is a Python library for compressing large language models via quantization, supporting CUDA on Linux and MPS on macOS; pick litgpt if litGPT offers extensive support for high-performance LLMs with comprehensive workflows for pretraining, fine-tuning, and deployment.
Markdown twin · OneCompression alternatives · litgpt alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | OneCompression | litgpt |
|---|---|---|
| Maintenance | Very active (1d since push) As of 3w · github_public_v1 | Active (17d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 3w · github_public_v1 | Not a fork · Organization account As of 2w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- OneCompression
- Python package for LLM compression
- litgpt
- High-performance LLMs with recipes for pretraining, finetuning and deployment
Stars
- OneCompression
- 398
- litgpt
- 14k
Forks
- OneCompression
- 18
- litgpt
- 1.5k
Open issues
- OneCompression
- 7
- litgpt
- 272
Language
- OneCompression
- Python
- litgpt
- Python
Adopt for
- OneCompression
- OneCompression is a Python library for compressing large language models via quantization, supporting CUDA on Linux and MPS on macOS.
- litgpt
- LitGPT offers extensive support for high-performance LLMs with comprehensive workflows for pretraining, fine-tuning, and deployment.
Persona
- OneCompression
- -
- litgpt
- -
Runtime
- OneCompression
- -
- litgpt
- -
License
- OneCompression
- MIT
- litgpt
- LitGPT operates under the open-source Apache-2.0 license, providing permissive terms for use and modification.
Last pushed
- OneCompression
- Jul 31, 2026
- litgpt
- Jul 20, 2026
Categories
- OneCompression
- LLM Frameworks, Model Training
- litgpt
- Inference & Serving, LLM Frameworks, Model Training
Trust and health
Maintenance
- OneCompression
- Very active (96%)
- litgpt
- Active (82%)
Days since push
- OneCompression
- 1d
- litgpt
- 17d
Open issues (now)
- OneCompression
- 7
- litgpt
- 272
Stars delta
- OneCompression
- Unknown
- litgpt
- +137 (30d)
Open issues delta
- OneCompression
- Unknown
- litgpt
- +6 (30d)
Full report
- OneCompression
- Trust report
- litgpt
- Trust report
Shared compatibility
- Python · OneCompression: Python runtime · litgpt: Python runtime
Choose OneCompression if…
- License: OneCompression is MIT, litgpt is Apache-2.0.
- Tags unique to OneCompression: compression, cuda, deepspeed, gptq.
- For CUDA quantum compression on Linux-based systems where PyTorch version 2.10 or later is required for vLLM serving with `cu130` index
When NOT to use OneCompression
- If your environment strictly requires CUDA versions other than 'cu130' as vLLM is only available with the latter
- When running on CPUs or non-Linux OS without NVIDIA GPU, since certain functionalities like vLLM serving and specific CUDA extras won't work
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: ai, artificial-intelligence, deep-learning, large language models.
- Also covers Inference & Serving.
- 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 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (FujitsuResearch/OneCompression) · observed Aug 2, 2026
- GitHub forks (FujitsuResearch/OneCompression) · observed Aug 2, 2026
- Last push (FujitsuResearch/OneCompression) · observed Jul 31, 2026
- License file (MIT) · observed Aug 2, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (Lightning-AI/litgpt) · observed Aug 7, 2026
- GitHub forks (Lightning-AI/litgpt) · observed Aug 7, 2026
- Last push (Lightning-AI/litgpt) · observed Jul 20, 2026
- License file (Apache-2.0) · observed Aug 7, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: OneCompression 398 · litgpt 14k (synced Aug 2, 2026).
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: compression, cuda, deepspeed, gptq; For CUDA quantum compression on Linux-based systems where PyTorch version 2.10 or later is required for vLLM serving with
cu130index. - 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: ai, artificial-intelligence, deep-learning, large language models; Also covers Inference & Serving; 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?
- If your environment strictly requires CUDA versions other than 'cu130' as vLLM is only available with the latter When running on CPUs or non-Linux OS without NVIDIA GPU, since certain functionalities like vLLM serving and specific CUDA extras won't work
- 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 (13,605 vs 398). 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 and litgpt alternatives (OneCompression markdown twin, litgpt markdown twin), 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 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: 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; litgpt trust report.