Learning to Shard: RL for Co-optimizing the Parallelism Degrees and Per-operator Sharding Dimensions in Distributed LLM Inference

Fuente: arXiv

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Bibliographic Details
Main Authors:	Yin, Ruokai, Mishra, Sattwik Deb, Zuo, Xuan, Tann, Hokchhay, Shah, Preyas, Guha, Apala
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Distributed, Parallel, and Cluster Computing
Online Access:	Acceder al recurso
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Internet

https://arxiv.org/abs/2509.00217