DECO: Sparse Mixture-of-Experts with Dense-Comparable Performance on End-Side Devices

arXiv:2605.10933v3 Announce Type: replace Abstract: While Mixture-of-Experts (MoE) scales model capacity without proportionally increasing computation, its massive total parameter footprint creates significant storage and memory-access bottlenecks, which hinder efficient end-side deployment that simultaneously requires high performance, low computational cost, and small storage overhead. To achieve these properties, we present DECO, a sparse MoE architecture designed to match the performance of dense Transformers under identical total parameter budgets and training tokens. DECO utilizes the di

Source: arXiv cs.LG — read the full report at the original publisher.

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