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

Fuente: arXiv

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Bibliographic Details
Main Authors:	Yao, Yuhang, Jin, Han, Shah, Alay Dilipbhai, Han, Shanshan, Hu, Zijian, Ran, Yide, Stripelis, Dimitris, Xu, Zhaozhuo, Avestimehr, Salman, He, Chaoyang
Format:	Preprint
Published:	2024
Subjects:	Distributed, Parallel, and Cluster Computing Machine Learning
Online Access:	Acceder al recurso
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Internet

https://arxiv.org/abs/2408.00008