arXiv:2610.37720v1 Announce Type: new Abstract: Sparse Mixture-of-Experts (MoE) models offer scalable capacity for Web applications, but uneven routing creates computation and communication bottlenecks. Existing placement and replication methods redistribute this workload, yet additional expert copies compete with token-related buffers for limited GPU memory. We present LRMoE, a memory-aware MoE t

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BuilderIf you train or fine-tune sparse MoE models on limited GPU memory, this demand-side recalibration approach may cut training time substantially versus SYMI.
ResearcherOffers a backward-demand recalibration metric and capacity-aware routing that preserves all routed forward computations while reducing backward traffic and buff

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