Comparison
airllm vs Star-Attention
Verdict
Pick airllm if airLLM is a notable framework designed specifically for running large language models on low-resource hardware, such as a single 4GB GPU; pick Star-Attention if star-Attention specializes in long sequence inference of large language models using star-attention to maintain efficiency.
Markdown twin · airllm alternatives · Star-Attention alternatives
GraphCanon updated 3w
vs
Trust & integrity
| Signal | airllm | Star-Attention |
|---|---|---|
| Maintenance | Very active (5d since push) As of 3w · github_public_v1 | Dormant (395d since push) As of 4w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 3w · github_public_v1 | Not a fork · Organization account As of 4w · github_public_v1 |
| OSV dependency advisories | Published findings 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
- airllm
- AirLLM 70B inference with single 4GB GPU
- Star-Attention
- Efficient LLM Inference over Long Sequences
Stars
- airllm
- 24k
- Star-Attention
- 392
Forks
- airllm
- 2.7k
- Star-Attention
- 24
Open issues
- airllm
- 115
- Star-Attention
- 0
Language
- airllm
- Jupyter Notebook
- Star-Attention
- Python
Adopt for
- airllm
- AirLLM is a notable framework designed specifically for running large language models on low-resource hardware, such as a single 4GB GPU.
- Star-Attention
- Star-Attention specializes in long sequence inference of large language models using star-attention to maintain efficiency.
Persona
- airllm
- -
- Star-Attention
- -
Runtime
- airllm
- -
- Star-Attention
- -
License
- airllm
- Apache-2.0
- Star-Attention
- Apache-2.0
Last pushed
- airllm
- Jul 23, 2026
- Star-Attention
- Jun 25, 2025
Categories
- airllm
- Inference & Serving
- Star-Attention
- Inference & Serving
Trust and health
Maintenance
- airllm
- Very active (96%)
- Star-Attention
- Dormant (18%)
Days since push
- airllm
- 5d
- Star-Attention
- 395d
Open issues (now)
- airllm
- 115
- Star-Attention
- 0
Owner type
- airllm
- User
- Star-Attention
- Organization
OSV dependency advisories
- airllm
- Published findings
- Star-Attention
- No lockfile (source not queried)
Full report
- airllm
- Trust report
- Star-Attention
- Trust report
Choose airllm if…
- airllm is primarily Jupyter Notebook; Star-Attention is Python.
- Pricing: Free and open-source under the Apache-2.0 license; however, infrastructure costs apply..
- Requirements: Min 16 GB RAM; A single 4GB GPU is sufficient for using this framework to run large language model inferences..
- Tags unique to airllm: chinese-llm, chinese-nlp, finetune, generative-ai.
- If you have limited hardware resources but need to perform inferences on large language models (like the 70B parameter model that AirLLM supports), use AirLLM.
When NOT to use airllm
- Avoid using AirLLM if you require models to run on higher-end GPUs or multiple GPU clusters, as its strength lies in low-resource efficiency.
- Do not use AirLLM if you are working primarily with non-Chinese language datasets and models, since support for other languages may be less optimized compared to competition.
Choose Star-Attention if…
- Star-Attention is primarily Python; airllm is Jupyter Notebook.
- Tags unique to Star-Attention: attention-mechanism, 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 (lyogavin/airllm) · observed Jul 28, 2026
- GitHub forks (lyogavin/airllm) · observed Jul 28, 2026
- Last push (lyogavin/airllm) · observed Jul 23, 2026
- License file (Apache-2.0) · observed Jul 28, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 9, 2026
- GitHub stars (NVIDIA/Star-Attention) · observed Jul 26, 2026
- GitHub forks (NVIDIA/Star-Attention) · observed Jul 26, 2026
- Last push (NVIDIA/Star-Attention) · observed Jun 25, 2025
- License file (Apache-2.0) · observed Jul 26, 2026
- Decision facts (enrichment) · observed Jul 15, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: airllm 24k · Star-Attention 392 (synced Jul 28, 2026).
Common questions
- What is the difference between airllm and Star-Attention?
- airllm: AirLLM 70B inference with single 4GB GPU. Star-Attention: Efficient LLM Inference over Long Sequences. See the comparison table for live GitHub stats and shared categories.
- When should I choose airllm over Star-Attention?
- Choose airllm over Star-Attention when airllm is primarily Jupyter Notebook; Star-Attention is Python; Pricing: Free and open-source under the Apache-2.0 license; however, infrastructure costs apply.; Requirements: Min 16 GB RAM; A single 4GB GPU is sufficient for using this framework to run large language model inferences.; Tags unique to airllm: chinese-llm, chinese-nlp, finetune, generative-ai; If you have limited hardware resources but need to perform inferences on large language models (like the 70B parameter model that AirLLM supports), use AirLLM.
- When should I choose Star-Attention over airllm?
- Choose Star-Attention over airllm when Star-Attention is primarily Python; airllm is Jupyter Notebook; Tags unique to Star-Attention: attention-mechanism, large language models, llm-inference; For applications requiring handling very large input sequences.
- When should I avoid airllm?
- Avoid using AirLLM if you require models to run on higher-end GPUs or multiple GPU clusters, as its strength lies in low-resource efficiency. Do not use AirLLM if you are working primarily with non-Chinese language datasets and models, since support for other languages may be less optimized compared to competition.
- 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 airllm or Star-Attention more popular on GitHub?
- airllm has more GitHub stars (24,183 vs 392). Stars measure visibility, not whether either tool fits your constraints.
- Are airllm and Star-Attention open source?
- Yes - both are open-source projects on GitHub (airllm: Apache-2.0, Star-Attention: Apache-2.0).
- Where can I find alternatives to airllm or Star-Attention?
- GraphCanon lists graph-backed alternatives at airllm alternatives and Star-Attention alternatives (airllm 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, airllm or Star-Attention?
- airllm: Very active. 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 airllm and Star-Attention?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: airllm trust report; Star-Attention trust report.