@DanKornas: Keeping up with LLM systems research is messy when papers, reports, frameworks, and course links are scattered everywhe…
Summary
LLMSys-PaperList is a curated reading list on GitHub that organizes LLM systems research papers and resources into practical categories such as training systems, serving systems, and multi-modal coverage, helping AI/ML engineers and researchers stay updated.
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Keeping up with LLM systems research is messy when papers, reports, frameworks, and course links are scattered everywhere.
LLMSys-PaperList is a curated LLM systems reading list for AI/ML engineers, researchers, and builders tracking how large language models are trained, served, and optimized.
It helps you follow the field by organizing papers and resources into practical systems categories instead of one flat bookmark dump.
Key features: • Training systems – pre-training, post-training/RLHF, fault tolerance, and straggler mitigation sections • Serving systems – LLM serving, agent systems, edge serving, and efficiency optimization links • Multi-modal coverage – separate training and serving sections for multi-modal systems • Research context – industrial LLM technical reports, survey papers, benchmarks, leaderboards, and traces • Learning path – frameworks, ML systems readings, MLSys courses, and conference-specific paper lists
Free public GitHub repo.
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