DecoMoE: Decoupling Visual Propagation and Expert Computation for Efficient Multimodal MoE Inference

Multimodal mixture-of-experts (MoE) models combine sparse expert activation with visual-language capabilities, yet their inference remains costly because long visual-token sequences repeatedly incur attention, routing, dispatch, and expert-MLP computation. Existing methods typically compress either