Comparison
yalm vs Awesome-LLM-Inference
Verdict
Pick yalm if yALM offers a no-frills LLM inference engine in C++/CUDA, optimized for tasks requiring minimal external dependencies beyond I/O and no reliance on heavyweight ML libraries; 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 · yalm alternatives · Awesome-LLM-Inference alternatives
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Trust & integrity
| Signal | yalm | Awesome-LLM-Inference |
|---|---|---|
| Maintenance | Slowing (315d since push) As of 1mo · github_public_v1 | Active (10d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Personal account As of 1mo · 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
- yalm
- LLM inference engine in C++/CUDA without dependency on external libraries except for I/O
- Awesome-LLM-Inference
- A curated list of LLM/VLM inference papers with codes
Stars
- yalm
- 592
- Awesome-LLM-Inference
- 5.5k
Forks
- yalm
- 64
- Awesome-LLM-Inference
- 429
Open issues
- yalm
- 4
- Awesome-LLM-Inference
- 6
Language
- yalm
- C++
- Awesome-LLM-Inference
- Python
Adopt for
- yalm
- YALM offers a no-frills LLM inference engine in C++/CUDA, optimized for tasks requiring minimal external dependencies beyond I/O and no reliance on heavyweight ML libraries.
- 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
- yalm
- -
- Awesome-LLM-Inference
- -
Runtime
- yalm
- -
- Awesome-LLM-Inference
- -
License
- yalm
- -
- 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
- yalm
- Sep 13, 2025
- Awesome-LLM-Inference
- Aug 14, 2026
Categories
- yalm
- Inference & Serving
- Awesome-LLM-Inference
- Inference & Serving
Trust and health
Maintenance
- yalm
- Slowing (36%)
- Awesome-LLM-Inference
- Active (82%)
Days since push
- yalm
- 315d
- Awesome-LLM-Inference
- 10d
Open issues (now)
- yalm
- 4
- Awesome-LLM-Inference
- 6
Stars delta
- yalm
- Unknown
- Awesome-LLM-Inference
- +62 (30d)
Open issues delta
- yalm
- Unknown
- Awesome-LLM-Inference
- 0 (30d)
Owner type
- yalm
- User
- Awesome-LLM-Inference
- Organization
Full report
- yalm
- Trust report
- Awesome-LLM-Inference
- Trust report
Choose yalm if…
- yalm is primarily C++; Awesome-LLM-Inference is Python.
- Tags unique to yalm: cpp, cuda, llm-inference, machine-learning.
- When your project's stack is primarily based on C++ and CUDA, allowing seamless integration without additional dependencies
When NOT to use yalm
- If extensive functionality or ease of use from other ML libraries is required, as YALM does not support dependencies beyond I/O needs
- For developers who prefer tools with broader community support and more comprehensive feature sets, given that YALM specializes in a narrow scope
Choose Awesome-LLM-Inference if…
- Awesome-LLM-Inference is primarily Python; yalm is C++.
- 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 (andrewkchan/yalm) · observed Jul 25, 2026
- GitHub forks (andrewkchan/yalm) · observed Jul 25, 2026
- Last push (andrewkchan/yalm) · observed Sep 13, 2025
- License file (unknown) · observed Jul 25, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (xlite-dev/Awesome-LLM-Inference) · observed Aug 24, 2026
- GitHub forks (xlite-dev/Awesome-LLM-Inference) · observed Aug 24, 2026
- Last push (xlite-dev/Awesome-LLM-Inference) · observed Aug 14, 2026
- License file (GPL-3.0) · observed Aug 24, 2026
- Decision facts (enrichment) · observed Jul 14, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: yalm 592 · Awesome-LLM-Inference 5.5k (synced Jul 25, 2026).
Common questions
- What is the difference between yalm and Awesome-LLM-Inference?
- yalm: LLM inference engine in C++/CUDA without dependency on external libraries except for I/O. 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 yalm over Awesome-LLM-Inference?
- Choose yalm over Awesome-LLM-Inference when yalm is primarily C++; Awesome-LLM-Inference is Python; Tags unique to yalm: cpp, cuda, llm-inference, machine-learning; When your project's stack is primarily based on C++ and CUDA, allowing seamless integration without additional dependencies.
- When should I choose Awesome-LLM-Inference over yalm?
- Choose Awesome-LLM-Inference over yalm when Awesome-LLM-Inference is primarily Python; yalm is C++; 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 yalm?
- If extensive functionality or ease of use from other ML libraries is required, as YALM does not support dependencies beyond I/O needs For developers who prefer tools with broader community support and more comprehensive feature sets, given that YALM specializes in a narrow scope
- 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 yalm or Awesome-LLM-Inference more popular on GitHub?
- Awesome-LLM-Inference has more GitHub stars (5,477 vs 592). Stars measure visibility, not whether either tool fits your constraints.
- Are yalm and Awesome-LLM-Inference open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to yalm or Awesome-LLM-Inference?
- GraphCanon lists graph-backed alternatives at yalm alternatives and Awesome-LLM-Inference alternatives (yalm 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, yalm or Awesome-LLM-Inference?
- yalm: Slowing. Awesome-LLM-Inference: 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 yalm and Awesome-LLM-Inference?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: yalm trust report; Awesome-LLM-Inference trust report.