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cs.CL2026
DPA-MoE: A Runtime Precision Control Framework for MoE Inference
Jaeyun Kim, Hoonki Lee, Susie Shin +3
Mixture-of-Experts (MoE) models reduce computation by routing each token to a small set of experts, but each selected expert is still used at a fixed precision. We present DPA-MoE,…
cs.LG2026
FlashMoE: Reducing SSD I/O Bottlenecks via ML-Based Cache Replacement for Mixture-of-Experts Inference on Edge Devices
Byeongju Kim, Jungwan Lee, Donghyeon Han +2
Recently, Mixture-of-Experts (MoE) models have gained attention for efficiently scaling large language models. Although these models are extremely large, their sparse activation en…