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Fanxin Li

1 paper hereh-index 386 citations7 works total

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  • cs.DC1

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1 paper

cs.DC2025

Themis: Efficient Sparse Model Training Through Fully Sharded Sparse Data Parallelism

Yuhao Qing, Guichao Zhu, Fanxin Li +10

Mixture-of-Experts (MoE) scales large language models cost-effectively, but expert-parallel training suffers severe straggler effects from skewed expert loads. Current systems freq…

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