Comparison
MInference vs Star-Attention
Verdict
Pick MInference if mInference accelerates long-context LLMs' inference by up to 10x via approximate sparse calculation techniques while preserving model accuracy; pick Star-Attention if star-Attention specializes in long sequence inference of large language models using star-attention to maintain efficiency.
Markdown twin · MInference alternatives · Star-Attention alternatives
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Trust & integrity
| Signal | MInference | Star-Attention |
|---|---|---|
| Maintenance | Slowing (120d since push) As of 2w · github_public_v1 | Dormant (425d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2w · github_public_v1 | Not a fork · Organization account As of today · 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
- MInference
- Accelerates Long-context LLMs' inference through approximate sparse calculation for attention.
- Star-Attention
- Efficient LLM Inference over Long Sequences
Stars
- MInference
- 1.2k
- Star-Attention
- 392
Forks
- MInference
- 80
- Star-Attention
- 25
Open issues
- MInference
- 93
- Star-Attention
- 1
Language
- MInference
- Python
- Star-Attention
- Python
Adopt for
- MInference
- MInference accelerates long-context LLMs' inference by up to 10x via approximate sparse calculation techniques while preserving model accuracy.
- Star-Attention
- Star-Attention specializes in long sequence inference of large language models using star-attention to maintain efficiency.
Persona
- MInference
- -
- Star-Attention
- -
Runtime
- MInference
- -
- Star-Attention
- -
License
- MInference
- MIT
- Star-Attention
- Apache-2.0
Last pushed
- MInference
- Apr 8, 2026
- Star-Attention
- Jun 25, 2025
Categories
- MInference
- Inference & Serving
- Star-Attention
- Inference & Serving
Trust and health
Maintenance
- MInference
- Slowing (36%)
- Star-Attention
- Dormant (18%)
Days since push
- MInference
- 120d
- Star-Attention
- 425d
Open issues (now)
- MInference
- 93
- Star-Attention
- 1
Stars delta
- MInference
- Unknown
- Star-Attention
- 0 (30d)
Open issues delta
- MInference
- Unknown
- Star-Attention
- +1 (30d)
Full report
- MInference
- Trust report
- Star-Attention
- Trust report
Choose MInference if…
- License: MInference is MIT, Star-Attention is Apache-2.0.
- Requirements: Min 8 GB RAM; MInference requires at least Torch and optionally FlashAttention-2 for maximum efficiency.; Triton for faster deployment and integration..
- Tags unique to MInference: flashattention-2, inference acceleration, long-context llms, sparse calculation.
- MInference is ideal for scenarios where significant reduction in inference latency is needed without sacrificing the accuracy of long-context LLM outputs.
When NOT to use MInference
- Avoid using MInference if your application does not benefit from or cannot tolerate slight variations in inference times due to its use of approximate sparse calculation.
- MInference might not be suitable for applications where the model's accuracy is critical and any reduction in the precision introduced by approximations would be detrimental.
Choose Star-Attention if…
- License: Star-Attention is Apache-2.0, MInference is MIT.
- Tags unique to Star-Attention: large language models, llm-inference.
- For applications requiring handling very large input sequences
When NOT to use Star-Attention
- If your use case involves short sequence processing only
- In scenarios where traditional attention mechanisms yield adequate results without performance loss
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (microsoft/MInference) · observed Aug 7, 2026
- GitHub forks (microsoft/MInference) · observed Aug 7, 2026
- Last push (microsoft/MInference) · observed Apr 8, 2026
- License file (MIT) · observed Aug 7, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (NVIDIA/Star-Attention) · observed Aug 25, 2026
- GitHub forks (NVIDIA/Star-Attention) · observed Aug 25, 2026
- Last push (NVIDIA/Star-Attention) · observed Jun 25, 2025
- License file (Apache-2.0) · observed Aug 25, 2026
- Decision facts (enrichment) · observed Jul 15, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: MInference 1.2k · Star-Attention 392 (synced Aug 7, 2026).
Common questions
- What is the difference between MInference and Star-Attention?
- MInference: Accelerates Long-context LLMs' inference through approximate sparse calculation for attention.. Star-Attention: Efficient LLM Inference over Long Sequences. See the comparison table for live GitHub stats and shared categories.
- When should I choose MInference over Star-Attention?
- Choose MInference over Star-Attention when License: MInference is MIT, Star-Attention is Apache-2.0; Requirements: Min 8 GB RAM; MInference requires at least Torch and optionally FlashAttention-2 for maximum efficiency.; Triton for faster deployment and integration.; Tags unique to MInference: flashattention-2, inference acceleration, long-context llms, sparse calculation; MInference is ideal for scenarios where significant reduction in inference latency is needed without sacrificing the accuracy of long-context LLM outputs.
- When should I choose Star-Attention over MInference?
- Choose Star-Attention over MInference when License: Star-Attention is Apache-2.0, MInference is MIT; Tags unique to Star-Attention: large language models, llm-inference; For applications requiring handling very large input sequences.
- When should I avoid MInference?
- Avoid using MInference if your application does not benefit from or cannot tolerate slight variations in inference times due to its use of approximate sparse calculation. MInference might not be suitable for applications where the model's accuracy is critical and any reduction in the precision introduced by approximations would be detrimental.
- When should I avoid Star-Attention?
- If your use case involves short sequence processing only In scenarios where traditional attention mechanisms yield adequate results without performance loss
- Is MInference or Star-Attention more popular on GitHub?
- MInference has more GitHub stars (1,225 vs 392). Stars measure visibility, not whether either tool fits your constraints.
- Are MInference and Star-Attention open source?
- Yes - both are open-source projects on GitHub (MInference: MIT, Star-Attention: Apache-2.0).
- Where can I find alternatives to MInference or Star-Attention?
- GraphCanon lists graph-backed alternatives at MInference alternatives and Star-Attention alternatives (MInference markdown twin, Star-Attention 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, MInference or Star-Attention?
- MInference: Slowing. Star-Attention: Dormant. 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 MInference and Star-Attention?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: MInference trust report; Star-Attention trust report.