arXiv AI By Arnab Kanti Tarafder, Jaume Guasch-Mart\'i, Gokcen Kestor, Jie Ren

Memory-Efficient Expert Routing for Distributed MoE Training

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The paper introduces RelayMoE, a ring‑based execution model for Mixture‑of‑Experts (MoE) training that reduces memory usage by avoiding full top‑k expanded dispatch buffers. RelayMoE dynamically chooses between expert and token routing based on communication volume and overlaps data transfer with computation, enabling efficient backward recomputation. Experiments on 30B–57B MoE models show up to a 2× speedup over Megatron‑LM and a 2.02× throughput improvement while extending trainable sequence lengths by up to 2.85×.

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