Comparison
Awesome-LLMs-ICLR-24 vs Awesome-LLM-Inference
Verdict
Pick Awesome-LLMs-ICLR-24 if awesome-LLMs-ICLR-24 is an essential resource hub for researchers and developers working with large language models, focusing on LLM research papers accepted at ICLR in 2024; 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 · Awesome-LLMs-ICLR-24 alternatives · Awesome-LLM-Inference alternatives
GraphCanon updated 1d
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Trust & integrity
| Signal | Awesome-LLMs-ICLR-24 | Awesome-LLM-Inference |
|---|---|---|
| Maintenance | Dormant (856d since push) As of 2w · github_public_v1 | Active (10d since push) As of 1d · github_public_v1 |
| Provenance | Not a fork · Personal account As of 2w · github_public_v1 | Not a fork · Organization account As of 1d · 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
- Awesome-LLMs-ICLR-24
- Compilation of LLM papers from ICLR 2024
- Awesome-LLM-Inference
- A curated list of LLM/VLM inference papers with codes
Stars
- Awesome-LLMs-ICLR-24
- 72
- Awesome-LLM-Inference
- 5.5k
Forks
- Awesome-LLMs-ICLR-24
- 5
- Awesome-LLM-Inference
- 429
Open issues
- Awesome-LLMs-ICLR-24
- 0
- Awesome-LLM-Inference
- 6
Language
- Awesome-LLMs-ICLR-24
- -
- Awesome-LLM-Inference
- Python
Adopt for
- Awesome-LLMs-ICLR-24
- Awesome-LLMs-ICLR-24 is an essential resource hub for researchers and developers working with large language models, focusing on LLM research papers accepted at ICLR in 2024.
- 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
- Awesome-LLMs-ICLR-24
- -
- Awesome-LLM-Inference
- -
Runtime
- Awesome-LLMs-ICLR-24
- -
- Awesome-LLM-Inference
- -
License
- Awesome-LLMs-ICLR-24
- MIT
- 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
- Awesome-LLMs-ICLR-24
- Apr 4, 2024
- Awesome-LLM-Inference
- Aug 14, 2026
Categories
- Awesome-LLMs-ICLR-24
- Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training
- Awesome-LLM-Inference
- Inference & Serving
Trust and health
Maintenance
- Awesome-LLMs-ICLR-24
- Dormant (18%)
- Awesome-LLM-Inference
- Active (82%)
Days since push
- Awesome-LLMs-ICLR-24
- 856d
- Awesome-LLM-Inference
- 10d
Open issues (now)
- Awesome-LLMs-ICLR-24
- 0
- Awesome-LLM-Inference
- 6
Stars delta
- Awesome-LLMs-ICLR-24
- Unknown
- Awesome-LLM-Inference
- +62 (30d)
Open issues delta
- Awesome-LLMs-ICLR-24
- Unknown
- Awesome-LLM-Inference
- 0 (30d)
Owner type
- Awesome-LLMs-ICLR-24
- User
- Awesome-LLM-Inference
- Organization
Full report
- Awesome-LLMs-ICLR-24
- Trust report
- Awesome-LLM-Inference
- Trust report
Choose Awesome-LLMs-ICLR-24 if…
- License: Awesome-LLMs-ICLR-24 is MIT, Awesome-LLM-Inference is GPL-3.0.
- Tags unique to Awesome-LLMs-ICLR-24: large-language-model, llm-agent, llm-evaluation, llm-framework.
- Also covers Developer Tools, Evaluation & Observability, LLM Frameworks, Model Training.
- If you are focusing specifically on recent advancements in Large Language Models discussed in the context of ICLR 2024, this repository will provide cutting-edge research papers and insights.
When NOT to use Awesome-LLMs-ICLR-24
- If you are looking for more general resources that cover a wider time span or different conferences than ICLR 2024.
- For projects where immediate practical application of models without understanding the underlying research is prioritized over detailed exploration and analysis.
Choose Awesome-LLM-Inference if…
- License: Awesome-LLM-Inference is GPL-3.0, Awesome-LLMs-ICLR-24 is MIT.
- 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 (azminewasi/Awesome-LLMs-ICLR-24) · observed Aug 8, 2026
- GitHub forks (azminewasi/Awesome-LLMs-ICLR-24) · observed Aug 8, 2026
- Last push (azminewasi/Awesome-LLMs-ICLR-24) · observed Apr 4, 2024
- License file (MIT) · observed Aug 8, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 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: Awesome-LLMs-ICLR-24 72 · Awesome-LLM-Inference 5.5k (synced Aug 8, 2026).
Common questions
- What is the difference between Awesome-LLMs-ICLR-24 and Awesome-LLM-Inference?
- Awesome-LLMs-ICLR-24: Compilation of LLM papers from ICLR 2024. 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 Awesome-LLMs-ICLR-24 over Awesome-LLM-Inference?
- Choose Awesome-LLMs-ICLR-24 over Awesome-LLM-Inference when License: Awesome-LLMs-ICLR-24 is MIT, Awesome-LLM-Inference is GPL-3.0; Tags unique to Awesome-LLMs-ICLR-24: large-language-model, llm-agent, llm-evaluation, llm-framework; Also covers Developer Tools, Evaluation & Observability, LLM Frameworks, Model Training; If you are focusing specifically on recent advancements in Large Language Models discussed in the context of ICLR 2024, this repository will provide cutting-edge research papers and insights.
- When should I choose Awesome-LLM-Inference over Awesome-LLMs-ICLR-24?
- Choose Awesome-LLM-Inference over Awesome-LLMs-ICLR-24 when License: Awesome-LLM-Inference is GPL-3.0, Awesome-LLMs-ICLR-24 is MIT; 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 Awesome-LLMs-ICLR-24?
- If you are looking for more general resources that cover a wider time span or different conferences than ICLR 2024. For projects where immediate practical application of models without understanding the underlying research is prioritized over detailed exploration and analysis.
- 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 Awesome-LLMs-ICLR-24 or Awesome-LLM-Inference more popular on GitHub?
- Awesome-LLM-Inference has more GitHub stars (5,477 vs 72). Stars measure visibility, not whether either tool fits your constraints.
- Are Awesome-LLMs-ICLR-24 and Awesome-LLM-Inference open source?
- Yes - both are open-source projects on GitHub (Awesome-LLMs-ICLR-24: MIT, Awesome-LLM-Inference: GPL-3.0).
- Where can I find alternatives to Awesome-LLMs-ICLR-24 or Awesome-LLM-Inference?
- GraphCanon lists graph-backed alternatives at Awesome-LLMs-ICLR-24 alternatives and Awesome-LLM-Inference alternatives (Awesome-LLMs-ICLR-24 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, Awesome-LLMs-ICLR-24 or Awesome-LLM-Inference?
- Awesome-LLMs-ICLR-24: Dormant. 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 Awesome-LLMs-ICLR-24 and Awesome-LLM-Inference?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Awesome-LLMs-ICLR-24 trust report; Awesome-LLM-Inference trust report.