Comparison
Awesome-LLMs-ICLR-24 vs Awesome-LLMOps
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-LLMOps if awesome-LLMOps is a curated list tailored for developers working with Large Language Models (LLMs), providing resources for model training, serving, evaluation, deployment, and more.
Markdown twin · Awesome-LLMs-ICLR-24 alternatives · Awesome-LLMOps alternatives
GraphCanon updated Sep 9, 2026
18views this month
vs
Trust & integrity
| Signal | Awesome-LLMs-ICLR-24 | Awesome-LLMOps |
|---|---|---|
| Maintenance | Dormant (887d since push) As of Sep 9, 2026 · github_public_v1 | Slowing (91d since push) As of Aug 20, 2026 · github_public_v1 |
| Provenance | Not a fork · Personal account As of Sep 9, 2026 · github_public_v1 | Not a fork · Organization account As of Aug 20, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 15, 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
- Awesome-LLMs-ICLR-24
- Compilation of LLM papers from ICLR 2024
- Awesome-LLMOps
- An awesome & curated list of best LLMOps tools for developers
Stars
- Awesome-LLMs-ICLR-24
- 72
- Awesome-LLMOps
- 5.9k
Forks
- Awesome-LLMs-ICLR-24
- 5
- Awesome-LLMOps
- 993
Open issues
- Awesome-LLMs-ICLR-24
- 0
- Awesome-LLMOps
- 247
Language
- Awesome-LLMs-ICLR-24
- -
- Awesome-LLMOps
- Shell
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-LLMOps
- Awesome-LLMOps is a curated list tailored for developers working with Large Language Models (LLMs), providing resources for model training, serving, evaluation, deployment, and more.
Persona
- Awesome-LLMs-ICLR-24
- -
- Awesome-LLMOps
- -
Runtime
- Awesome-LLMs-ICLR-24
- -
- Awesome-LLMOps
- -
License
- Awesome-LLMs-ICLR-24
- MIT
- Awesome-LLMOps
- CC0-1.0
Last pushed
- Awesome-LLMs-ICLR-24
- Apr 4, 2024
- Awesome-LLMOps
- May 21, 2026
Categories
- Awesome-LLMs-ICLR-24
- Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training
- Awesome-LLMOps
- Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio
Trust and health
Maintenance
- Awesome-LLMs-ICLR-24
- Dormant (18%)
- Awesome-LLMOps
- Slowing (36%)
Days since push
- Awesome-LLMs-ICLR-24
- 887d
- Awesome-LLMOps
- 91d
Open issues (now)
- Awesome-LLMs-ICLR-24
- 0
- Awesome-LLMOps
- 247
Stars delta
- Awesome-LLMs-ICLR-24
- 0 (30d)
- Awesome-LLMOps
- +28 (30d)
Open issues delta
- Awesome-LLMs-ICLR-24
- 0 (30d)
- Awesome-LLMOps
- +66 (30d)
Owner type
- Awesome-LLMs-ICLR-24
- User
- Awesome-LLMOps
- Organization
Full report
- Awesome-LLMs-ICLR-24
- Trust report
- Awesome-LLMOps
- Trust report
Choose Awesome-LLMs-ICLR-24 if…
- License: Awesome-LLMs-ICLR-24 is MIT, Awesome-LLMOps is CC0-1.0.
- Tags unique to Awesome-LLMs-ICLR-24: large-language-model, llm-agent, llm-evaluation, llm-framework.
- Also covers Developer Tools.
- 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-LLMOps if…
- License: Awesome-LLMOps is CC0-1.0, Awesome-LLMs-ICLR-24 is MIT.
- Tags unique to Awesome-LLMOps: ai development tools, awesome-list, llmops, mlops.
- Also covers Computer Vision, Data & Retrieval, Speech & Audio.
- - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management.
When NOT to use Awesome-LLMOps
- - When you are looking for a hands-on platform or framework for developing and deploying models rather than just a resource list.
- - If your focus is on general artificial intelligence development that includes areas beyond LLMOps like image processing, robotics, or federated learning without the need for LLM-specific resources.
Explore
Awesome-LLMs-ICLR-24 trust report →Awesome-LLMOps trust report →Developer Tools category →Evaluation & Observability category →Inference & Serving category →LLM Frameworks category →Model Training category →Computer Vision category →Data & Retrieval category →Speech & Audio category →All comparisonsStack workflowsTrending tools
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 Sep 9, 2026
- GitHub forks (azminewasi/Awesome-LLMs-ICLR-24) · observed Sep 9, 2026
- Last push (azminewasi/Awesome-LLMs-ICLR-24) · observed Apr 4, 2024
- License file (MIT) · observed Sep 9, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
- GitHub stars (tensorchord/Awesome-LLMOps) · observed Aug 20, 2026
- GitHub forks (tensorchord/Awesome-LLMOps) · observed Aug 20, 2026
- Last push (tensorchord/Awesome-LLMOps) · observed May 21, 2026
- License file (CC0-1.0) · observed Aug 20, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: Awesome-LLMs-ICLR-24 72 · Awesome-LLMOps 5.9k (synced Sep 9, 2026).
Common questions
- What is the difference between Awesome-LLMs-ICLR-24 and Awesome-LLMOps?
- Awesome-LLMs-ICLR-24: Compilation of LLM papers from ICLR 2024. Awesome-LLMOps: An awesome & curated list of best LLMOps tools for developers. See the comparison table for live GitHub stats and shared categories.
- When should I choose Awesome-LLMs-ICLR-24 over Awesome-LLMOps?
- Choose Awesome-LLMs-ICLR-24 over Awesome-LLMOps when License: Awesome-LLMs-ICLR-24 is MIT, Awesome-LLMOps is CC0-1.0; Tags unique to Awesome-LLMs-ICLR-24: large-language-model, llm-agent, llm-evaluation, llm-framework; Also covers Developer Tools; 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-LLMOps over Awesome-LLMs-ICLR-24?
- Choose Awesome-LLMOps over Awesome-LLMs-ICLR-24 when License: Awesome-LLMOps is CC0-1.0, Awesome-LLMs-ICLR-24 is MIT; Tags unique to Awesome-LLMOps: ai development tools, awesome-list, llmops, mlops; Also covers Computer Vision, Data & Retrieval, Speech & Audio; - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management.
- 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-LLMOps?
- - When you are looking for a hands-on platform or framework for developing and deploying models rather than just a resource list. - If your focus is on general artificial intelligence development that includes areas beyond LLMOps like image processing, robotics, or federated learning without the need for LLM-specific resources.
- Is Awesome-LLMs-ICLR-24 or Awesome-LLMOps more popular on GitHub?
- Awesome-LLMOps has more GitHub stars (5,915 vs 72). Stars measure visibility, not whether either tool fits your constraints.
- Are Awesome-LLMs-ICLR-24 and Awesome-LLMOps open source?
- Yes - both are open-source projects on GitHub (Awesome-LLMs-ICLR-24: MIT, Awesome-LLMOps: CC0-1.0).
- Where can I find alternatives to Awesome-LLMs-ICLR-24 or Awesome-LLMOps?
- GraphCanon lists graph-backed alternatives at Awesome-LLMs-ICLR-24 alternatives and Awesome-LLMOps alternatives (Awesome-LLMs-ICLR-24 markdown twin, Awesome-LLMOps 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-LLMOps?
- Awesome-LLMs-ICLR-24: Dormant. Awesome-LLMOps: Slowing. 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-LLMOps?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Awesome-LLMs-ICLR-24 trust report; Awesome-LLMOps trust report.