LLM Inference Serving: Survey of Recent Advances and Opportunities
This survey offers a comprehensive overview of recent advancements in Large Language Model (LLM) serving systems, focusing on research since the year 2023. We specifically examine system-level enhancements that improve performance and efficiency without altering the core LLM decoding mechanisms. By selecting and reviewing high-quality papers from prestigious ML and system venues, we highlight key innovations and practical considerations for deploying and scaling LLMs in real-world production environments. This survey serves as a valuable resource for LLM practitioners seeking to stay abreast of the latest developments in this rapidly evolving field.
Authors
- Vijay Gadepally (ORCID: https://orcid.org/0000-0002-4598-2808)
- Devesh Tiwari
- Baolin Li (ORCID: https://orcid.org/0000-0001-9778-1023)
- Yankai Jiang (ORCID: https://orcid.org/0009-0006-9968-7560)
Institutions
- MIT Lincoln Laboratory (US)
- Universidad del Noreste (MX)
Publication Details
- Published
- 2024-09-23
- DOI
- https://doi.org/10.1109/hpec62836.2024.10938426
- Citations
- 18
- Primary Topic
- Data Quality and Management
- Type
- article
- Field-Weighted Citation Impact
- 8.83