[go: up one dir, main page]

ScaleLLM: A Resource-Frugal LLM Serving Framework by Optimizing End-to-End Efficiency

Yuhang Yao, Han Jin, Alay Dilipbhai Shah, Shanshan Han, Zijian Hu, Dimitris Stripelis, Yide Ran, Zhaozhuo Xu, Salman Avestimehr, Chaoyang He


Abstract
Large language models (LLMs) have surged in popularity and are extensively used in commercial applications, where the efficiency of model serving is crucial for the user experience. Most current research focuses on optimizing individual sub-procedures, e.g. local inference and communication, however, there is no comprehensive framework that provides a holistic system view for optimizing LLM serving in an end-to-end manner. In this work, we conduct a detailed analysis to identify major bottlenecks that impact end-to-end latency in LLM serving systems. Our analysis reveals that a comprehensive LLM serving endpoint must address a series of efficiency bottlenecks that extend beyond LLM inference. We then propose ScaleLLM, an optimized system for resource-efficient LLM serving. Our extensive experiments reveal that reveal that with 64 concurrent requests on Mixtral 8x7B, ScaleLLM achieves a 4.3× speed up over vLLM and outperforms state-of-the-arts with 1.5× higher throughput.
Anthology ID:
2024.emnlp-industry.22
Volume:
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track
Month:
November
Year:
2024
Address:
Miami, Florida, US
Editors:
Franck Dernoncourt, Daniel Preoţiuc-Pietro, Anastasia Shimorina
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
279–289
Language:
URL:
https://aclanthology.org/2024.emnlp-industry.22
DOI:
10.18653/v1/2024.emnlp-industry.22
Bibkey:
Cite (ACL):
Yuhang Yao, Han Jin, Alay Dilipbhai Shah, Shanshan Han, Zijian Hu, Dimitris Stripelis, Yide Ran, Zhaozhuo Xu, Salman Avestimehr, and Chaoyang He. 2024. ScaleLLM: A Resource-Frugal LLM Serving Framework by Optimizing End-to-End Efficiency. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 279–289, Miami, Florida, US. Association for Computational Linguistics.
Cite (Informal):
ScaleLLM: A Resource-Frugal LLM Serving Framework by Optimizing End-to-End Efficiency (Yao et al., EMNLP 2024)
Copy Citation:
PDF:
https://aclanthology.org/2024.emnlp-industry.22.pdf
Presentation:
 2024.emnlp-industry.22.presentation.pdf