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.

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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
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article

LLM Inference Serving: Survey of Recent Advances and Opportunities

Vijay Gadepally, Devesh Tiwari, Baolin Li, Yankai Jiang
18 citations
Data Quality and Management
8.83
article

LLM Inference Serving: Survey of Recent Advances and Opportunities

Vijay Gadepally, Devesh Tiwari, Baolin Li, Yankai Jiang
article en
18 citations

Abstract

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.

MIT Lincoln Laboratory (US), Universidad del Noreste (MX)
Openalex Percentile: Top 2%
Data Quality and Management
8.83
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