May 14, 2024, 4:44 a.m. | Jing Li, Zhijie Sun, Xuan He, Li Zeng, Yi Lin, Entong Li, Binfan Zheng, Rongqian Zhao, Xin Chen

cs.LG updates on

arXiv:2401.13920v2 Announce Type: replace
Abstract: The Mixtures-of-Experts (MoE) model is a widespread distributed and integrated learning method for large language models (LLM), which is favored due to its ability to sparsify and expand models efficiently. However, the performance of MoE is limited by load imbalance and high latency of All-to-All communication, along with relatively redundant computation owing to large expert capacity. Load imbalance may result from existing routing policies that consistently tend to select certain experts. The frequent inter-node communication …

abstract arxiv communication cs.lg distributed expand experts however language language model language models language model training large language large language model large language models latency llm low moe performance replace training type

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