Comparison
llmfit vs OneCompression
Verdict
Pick llmfit if llmfit is a Rust-based tool that aims to streamline the process of discovering and managing machine learning models based solely on the hardware capabilities available; pick OneCompression if oneCompression is a Python library for compressing large language models via quantization, supporting CUDA on Linux and MPS on macOS.
Markdown twin · llmfit alternatives · OneCompression alternatives
GraphCanon updated 1w
vs
Trust & integrity
| Signal | llmfit | OneCompression |
|---|---|---|
| Maintenance | Very active (2d since push) As of 1w · github_public_v1 | Very active (1d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 1w · github_public_v1 | Not a fork · Organization account As of 3w · 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
- llmfit
- Hundreds of models & providers. One command to find what runs on your hardware.
- OneCompression
- Python package for LLM compression
Stars
- llmfit
- 32k
- OneCompression
- 398
Forks
- llmfit
- 2.0k
- OneCompression
- 18
Open issues
- llmfit
- 69
- OneCompression
- 7
Language
- llmfit
- Rust
- OneCompression
- Python
Adopt for
- llmfit
- llmfit is a Rust-based tool that aims to streamline the process of discovering and managing machine learning models based solely on the hardware capabilities available.
- OneCompression
- OneCompression is a Python library for compressing large language models via quantization, supporting CUDA on Linux and MPS on macOS.
Persona
- llmfit
- -
- OneCompression
- -
Runtime
- llmfit
- -
- OneCompression
- -
License
- llmfit
- MIT License. This means it's open-source, permitting use in multiple contexts like commercial projects without charge.
- OneCompression
- MIT
Last pushed
- llmfit
- Aug 14, 2026
- OneCompression
- Jul 31, 2026
Categories
- llmfit
- LLM Frameworks, Model Training
- OneCompression
- LLM Frameworks, Model Training
Trust and health
Days since push
- llmfit
- 2d
- OneCompression
- 1d
Open issues (now)
- llmfit
- 69
- OneCompression
- 7
Stars delta
- llmfit
- +2.3k (30d)
- OneCompression
- Unknown
Open issues delta
- llmfit
- +19 (30d)
- OneCompression
- Unknown
Owner type
- llmfit
- User
- OneCompression
- Organization
Full report
- llmfit
- Trust report
- OneCompression
- Trust report
Choose llmfit if…
- llmfit is primarily Rust; OneCompression is Python.
- Requirements: Min 4 GB RAM; Built for Rust environments; No explicit dependency on Docker or other container runtimes.
- Tags unique to llmfit: gguf, localai, mlx, skill.
- llmfit ships Docker support for self-hosted deployment.
- - When you need to quickly identify compatible machine learning models for your specific hardware configuration without manual research. llmfit automates this process, making it efficient.
When NOT to use llmfit
- - When the focus is on model development rather than discovery or management; llmfit centers on finding models based on hardware but does not provide deep integration into the training process itself.
- - If real-time adaptability and dynamic hardware compatibility changes are needed, as llmfit operates with a more static approach tied to one command per execution.
Choose OneCompression if…
- OneCompression is primarily Python; llmfit is Rust.
- 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
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (AlexsJones/llmfit) · observed Aug 16, 2026
- GitHub forks (AlexsJones/llmfit) · observed Aug 16, 2026
- Last push (AlexsJones/llmfit) · observed Aug 14, 2026
- License file (MIT) · observed Aug 16, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- 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 on cards: llmfit 32k · OneCompression 398 (synced Aug 16, 2026).
Common questions
- What is the difference between llmfit and OneCompression?
- llmfit: Hundreds of models & providers. One command to find what runs on your hardware.. OneCompression: Python package for LLM compression. See the comparison table for live GitHub stats and shared categories.
- When should I choose llmfit over OneCompression?
- Choose llmfit over OneCompression when llmfit is primarily Rust; OneCompression is Python; Requirements: Min 4 GB RAM; Built for Rust environments; No explicit dependency on Docker or other container runtimes; Tags unique to llmfit: gguf, localai, mlx, skill; llmfit ships Docker support for self-hosted deployment; - When you need to quickly identify compatible machine learning models for your specific hardware configuration without manual research. llmfit automates this process, making it efficient.
- When should I choose OneCompression over llmfit?
- Choose OneCompression over llmfit when OneCompression is primarily Python; llmfit is Rust; 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 avoid llmfit?
- - When the focus is on model development rather than discovery or management; llmfit centers on finding models based on hardware but does not provide deep integration into the training process itself. - If real-time adaptability and dynamic hardware compatibility changes are needed, as llmfit operates with a more static approach tied to one command per execution.
- 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
- Is llmfit or OneCompression more popular on GitHub?
- llmfit has more GitHub stars (31,867 vs 398). Stars measure visibility, not whether either tool fits your constraints.
- Are llmfit and OneCompression open source?
- Yes - both are open-source projects on GitHub (llmfit: MIT, OneCompression: MIT).
- Where can I find alternatives to llmfit or OneCompression?
- GraphCanon lists graph-backed alternatives at llmfit alternatives and OneCompression alternatives (llmfit markdown twin, OneCompression 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, llmfit or OneCompression?
- llmfit: Very 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 llmfit and OneCompression?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: llmfit trust report; OneCompression trust report.