Comparison
LLMSys-PaperList vs awesome-mlops
Verdict
Pick LLMSys-PaperList if lLMSys-PaperList offers a comprehensive list of papers and resources tailored specifically to Large Language Model (LLM) systems; pick awesome-mlops if awesome-mlops curates MLOps resources focusing on diverse deployment strategies and tooling.
Markdown twin · LLMSys-PaperList alternatives · awesome-mlops alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | LLMSys-PaperList | awesome-mlops |
|---|---|---|
| Maintenance | Active (12d since push) As of 2w · github_public_v1 | Dormant (621d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 2w · github_public_v1 | Not a fork · Personal account As of 3w · 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
- LLMSys-PaperList
- Curated list of academic papers related to Large Language Model systems
- awesome-mlops
- A curated list of references for MLOps
Stars
- LLMSys-PaperList
- 2.2k
- awesome-mlops
- 14k
Forks
- LLMSys-PaperList
- 120
- awesome-mlops
- 2.1k
Open issues
- LLMSys-PaperList
- 1
- awesome-mlops
- 44
Language
- LLMSys-PaperList
- Python
- awesome-mlops
- -
Adopt for
- LLMSys-PaperList
- LLMSys-PaperList offers a comprehensive list of papers and resources tailored specifically to Large Language Model (LLM) systems.
- awesome-mlops
- awesome-mlops curates MLOps resources focusing on diverse deployment strategies and tooling.
Persona
- LLMSys-PaperList
- -
- awesome-mlops
- -
Runtime
- LLMSys-PaperList
- -
- awesome-mlops
- -
License
- LLMSys-PaperList
- (unknown)
- awesome-mlops
- -
Last pushed
- LLMSys-PaperList
- Jul 25, 2026
- awesome-mlops
- Nov 21, 2024
Categories
- LLMSys-PaperList
- Inference & Serving, LLM Frameworks, Model Training
- awesome-mlops
- Inference & Serving, Model Training
Trust and health
Maintenance
- LLMSys-PaperList
- Active (82%)
- awesome-mlops
- Dormant (18%)
Days since push
- LLMSys-PaperList
- 12d
- awesome-mlops
- 621d
Open issues (now)
- LLMSys-PaperList
- 1
- awesome-mlops
- 44
Full report
- LLMSys-PaperList
- Trust report
- awesome-mlops
- Trust report
Choose LLMSys-PaperList if…
- (repository does not specify hosting environment)
- Tags unique to LLMSys-PaperList: academic-sources, framework-overview, inference-techniques, research papers.
- Also covers LLM Frameworks.
- - When you need a curated list focusing on technical advancements in pre-training, post-training, serving, and multi-modal LLM systems.
When NOT to use LLMSys-PaperList
- - If you are looking for a general repository of machine learning papers rather than specific developments related to Large Language Models.
- - When your primary need is documentation or code examples rather than academic papers and project insights.
- - For applications where real-time updates and active community support are imperative, as LLMSys-PaperList primarily serves as a static list without user interaction features like commenting or liveＱ
Choose awesome-mlops if…
- Tags unique to awesome-mlops: ai, data-science, devops, engineering.
- If you need references covering online training and inference service architecture patterns, consider awesome-mlops.
- More GitHub stars (14k vs 2.2k) - visibility, not fit.
When NOT to use awesome-mlops
- Avoid if focused solely on a single MLOps tool or framework as this is a broad resource list.
- Not suitable for those seeking end-to-end support beyond references, like hands-on deployment assistance.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (AmberLJC/LLMSys-PaperList) · observed Aug 6, 2026
- GitHub forks (AmberLJC/LLMSys-PaperList) · observed Aug 6, 2026
- Last push (AmberLJC/LLMSys-PaperList) · observed Jul 25, 2026
- License file (unknown) · observed Aug 6, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (visenger/awesome-mlops) · observed Aug 4, 2026
- GitHub forks (visenger/awesome-mlops) · observed Aug 4, 2026
- Last push (visenger/awesome-mlops) · observed Nov 21, 2024
- License file (unknown) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: LLMSys-PaperList 2.2k · awesome-mlops 14k (synced Aug 6, 2026).
Common questions
- What is the difference between LLMSys-PaperList and awesome-mlops?
- LLMSys-PaperList: Curated list of academic papers related to Large Language Model systems. awesome-mlops: A curated list of references for MLOps. See the comparison table for live GitHub stats and shared categories.
- When should I choose LLMSys-PaperList over awesome-mlops?
- Choose LLMSys-PaperList over awesome-mlops when (repository does not specify hosting environment); Tags unique to LLMSys-PaperList: academic-sources, framework-overview, inference-techniques, research papers; Also covers LLM Frameworks; - When you need a curated list focusing on technical advancements in pre-training, post-training, serving, and multi-modal LLM systems.
- When should I choose awesome-mlops over LLMSys-PaperList?
- Choose awesome-mlops over LLMSys-PaperList when Tags unique to awesome-mlops: ai, data-science, devops, engineering; If you need references covering online training and inference service architecture patterns, consider awesome-mlops; More GitHub stars (14k vs 2.2k) - visibility, not fit.
- When should I avoid LLMSys-PaperList?
- - If you are looking for a general repository of machine learning papers rather than specific developments related to Large Language Models. - When your primary need is documentation or code examples rather than academic papers and project insights. - For applications where real-time updates and active community support are imperative, as LLMSys-PaperList primarily serves as a static list without user interaction features like commenting or liveＱ
- When should I avoid awesome-mlops?
- Avoid if focused solely on a single MLOps tool or framework as this is a broad resource list. Not suitable for those seeking end-to-end support beyond references, like hands-on deployment assistance.
- Is LLMSys-PaperList or awesome-mlops more popular on GitHub?
- awesome-mlops has more GitHub stars (14,127 vs 2,220). Stars measure visibility, not whether either tool fits your constraints.
- Are LLMSys-PaperList and awesome-mlops open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to LLMSys-PaperList or awesome-mlops?
- GraphCanon lists graph-backed alternatives at LLMSys-PaperList alternatives and awesome-mlops alternatives (LLMSys-PaperList markdown twin, awesome-mlops 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, LLMSys-PaperList or awesome-mlops?
- LLMSys-PaperList: Active. awesome-mlops: 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 LLMSys-PaperList and awesome-mlops?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: LLMSys-PaperList trust report; awesome-mlops trust report.