Comparison
optillm vs Awesome-LLM-Inference
Verdict
Pick optillm if optillm is an optimizing inference proxy for LLMs that provides enhanced deployment options through Docker, supporting both full and lightweight configurations; pick Awesome-LLM-Inference if awesome-LLM-Inference is a well-curated list of papers and codes related to efficient inference techniques for large language models and vision-language models, featuring methods like Flash-Attention and Paged-Attention.
Markdown twin · optillm alternatives · Awesome-LLM-Inference alternatives
GraphCanon updated Sep 20, 2026
16views this month
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Trust & integrity
| Signal | optillm | Awesome-LLM-Inference |
|---|---|---|
| Maintenance | Steady (30d since push) As of Aug 17, 2026 · github_public_v1 | Steady (36d since push) As of Sep 19, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Aug 17, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 19, 2026 · github_public_v1 |
| OSV dependency advisories | Published findings As of Jul 11, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 11, 2026 · 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
- optillm
- Optimizing inference proxy for LLMs
- Awesome-LLM-Inference
- A curated list of LLM/VLM inference papers with codes
Stars
- optillm
- 4.2k
- Awesome-LLM-Inference
- 5.5k
Forks
- optillm
- 385
- Awesome-LLM-Inference
- 435
Open issues
- optillm
- 25
- Awesome-LLM-Inference
- 8
Language
- optillm
- Python
- Awesome-LLM-Inference
- Python
Adopt for
- optillm
- optillm is an optimizing inference proxy for LLMs that provides enhanced deployment options through Docker, supporting both full and lightweight configurations.
- Awesome-LLM-Inference
- Awesome-LLM-Inference is a well-curated list of papers and codes related to efficient inference techniques for large language models and vision-language models, featuring methods like Flash-Attention and Paged-Attention.
Persona
- optillm
- -
- Awesome-LLM-Inference
- -
Runtime
- optillm
- -
- Awesome-LLM-Inference
- -
License
- optillm
- Apache-2.0
- Awesome-LLM-Inference
- The tool is licensed under GPL-3.0, which may affect how it can be integrated into other projects depending on their licensing needs.
Last pushed
- optillm
- Jul 18, 2026
- Awesome-LLM-Inference
- Aug 14, 2026
Categories
- optillm
- Inference & Serving
- Awesome-LLM-Inference
- Inference & Serving
Trust and health
Days since push
- optillm
- 30d
- Awesome-LLM-Inference
- 36d
Open issues (now)
- optillm
- 25
- Awesome-LLM-Inference
- 8
Stars delta
- optillm
- +67 (30d)
- Awesome-LLM-Inference
- +93 (30d)
Open issues delta
- optillm
- +5 (30d)
- Awesome-LLM-Inference
- +2 (30d)
OSV dependency advisories
- optillm
- Published findings
- Awesome-LLM-Inference
- No lockfile (source not queried)
Full report
- optillm
- Trust report
- Awesome-LLM-Inference
- Trust report
Choose optillm if…
- License: optillm is Apache-2.0, Awesome-LLM-Inference is GPL-3.0.
- This open-source proxy supports diverse hosting environments and can be run via Docker for flexibility in deployment.
- Pricing: optillm is available under the Apache-2.0 license, which makes it free to use and distribute without cost..
- Tags unique to optillm: agent, agentic-ai, genai, llm-inference.
- optillm ships Docker support for self-hosted deployment.
- Use optillm when you require automatic optimization of the server approach to enhance reasoning capabilities with large language models.
When NOT to use optillm
- Avoid optillm when your application does not require proxy server optimization for large language models; simpler serving setups may suffice.
- Do not use optillm if your deployment environment strictly prohibits the use of Docker images or containers, given that this tool heavily relies on Docker for its various configurations.
Choose Awesome-LLM-Inference if…
- License: Awesome-LLM-Inference is GPL-3.0, optillm is Apache-2.0.
- Requirements: Requires Python for the use of included codes and to understand the methods described in the associated papers..
- Tags unique to Awesome-LLM-Inference: flash-attention, paged-attention, parallelism, wint8/4.
- Use Awesome-LLM-Inference when you are looking to optimize the performance of your large language model or vision-language model inference with cutting-edge techniques such as Flash-Attention.
When NOT to use Awesome-LLM-Inference
- Do not use Awesome-LLM-Inference if your project strictly conforms to licenses different from GPL-3.0, as its licensing could be incompatible with your project's license requirements.
- Avoid using this tool for immediate production implementation of inference techniques without additional vetting since the repository itself may contain unvetted research papers and code snippets.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (algorithmicsuperintelligence/optillm) · observed Sep 20, 2026
- GitHub forks (algorithmicsuperintelligence/optillm) · observed Sep 20, 2026
- Last push (algorithmicsuperintelligence/optillm) · observed Jul 18, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 14, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (xlite-dev/Awesome-LLM-Inference) · observed Sep 20, 2026
- GitHub forks (xlite-dev/Awesome-LLM-Inference) · observed Sep 20, 2026
- Last push (xlite-dev/Awesome-LLM-Inference) · observed Aug 14, 2026
- License file (GPL-3.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 14, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: optillm 4.2k · Awesome-LLM-Inference 5.5k (synced Sep 20, 2026).
Common questions
- What is the difference between optillm and Awesome-LLM-Inference?
- optillm: Optimizing inference proxy for LLMs. Awesome-LLM-Inference: A curated list of LLM/VLM inference papers with codes. See the comparison table for live GitHub stats and shared categories.
- When should I choose optillm over Awesome-LLM-Inference?
- Choose optillm over Awesome-LLM-Inference when License: optillm is Apache-2.0, Awesome-LLM-Inference is GPL-3.0; This open-source proxy supports diverse hosting environments and can be run via Docker for flexibility in deployment; Pricing: optillm is available under the Apache-2.0 license, which makes it free to use and distribute without cost.; Tags unique to optillm: agent, agentic-ai, genai, llm-inference; optillm ships Docker support for self-hosted deployment; Use optillm when you require automatic optimization of the server approach to enhance reasoning capabilities with large language models.
- When should I choose Awesome-LLM-Inference over optillm?
- Choose Awesome-LLM-Inference over optillm when License: Awesome-LLM-Inference is GPL-3.0, optillm is Apache-2.0; Requirements: Requires Python for the use of included codes and to understand the methods described in the associated papers.; Tags unique to Awesome-LLM-Inference: flash-attention, paged-attention, parallelism, wint8/4; Use Awesome-LLM-Inference when you are looking to optimize the performance of your large language model or vision-language model inference with cutting-edge techniques such as Flash-Attention.
- When should I avoid optillm?
- Avoid optillm when your application does not require proxy server optimization for large language models; simpler serving setups may suffice. Do not use optillm if your deployment environment strictly prohibits the use of Docker images or containers, given that this tool heavily relies on Docker for its various configurations.
- When should I avoid Awesome-LLM-Inference?
- Do not use Awesome-LLM-Inference if your project strictly conforms to licenses different from GPL-3.0, as its licensing could be incompatible with your project's license requirements. Avoid using this tool for immediate production implementation of inference techniques without additional vetting since the repository itself may contain unvetted research papers and code snippets.
- Is optillm or Awesome-LLM-Inference more popular on GitHub?
- Awesome-LLM-Inference has more GitHub stars (5,508 vs 4,244). Stars measure visibility, not whether either tool fits your constraints.
- Are optillm and Awesome-LLM-Inference open source?
- Yes - both are open-source projects on GitHub (optillm: Apache-2.0, Awesome-LLM-Inference: GPL-3.0).
- Where can I find alternatives to optillm or Awesome-LLM-Inference?
- GraphCanon lists graph-backed alternatives at optillm alternatives and Awesome-LLM-Inference alternatives (optillm markdown twin, Awesome-LLM-Inference 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, optillm or Awesome-LLM-Inference?
- optillm: Steady. Awesome-LLM-Inference: Steady. 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 optillm and Awesome-LLM-Inference?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: optillm trust report; Awesome-LLM-Inference trust report.