SpecPrefetch: Parameter-Efficient Expert Prefetching for Sparse MoE Foundation Models
A new framework optimizes sparse MoE model deployment on edge devices by predicting expert usage ahead of time.
Sparse Mixture-of-Experts (MoE) models expand foundation model capacity through conditional expert activation, but their full expert pools remain difficult to deploy under limited accelerator memory. Although expert offloading alleviates memory pressure by moving inactive experts...
ArXiv CS.AI
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SpecPrefetch bridges the hardware-software gap by enabling asynchronous loading of experts, effectively hiding memory transfer latency in sparse models. This approach significantly democratizes the use of massive Mixture-of-Experts models on resource-constrained devices without sacrificing output accuracy.
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