APEX: Adaptive Expert Prefetching for Memory-Efficient Edge MoE Inference
Mixture-of-Experts (MoE) models are attractive for edge deployment because they provide high model capacity while activating only a small subset of parameters per token, improving compute efficiency. However, MoE inference at the edge is fundamentally limited by memory. Expert parameters are large and often reside in off-chip memory due to capacity, cost, and power constraints, putting expert loading to the critical path. We present APEX: Adaptive Expert Prefetching, a predictive resource management framework that overlaps expert loading with useful computation. APEX introduces a lightweight prefetch router that predicts candidate experts before the attention block to dynamically fetch ad...
| Authors | Alish Kanani, Layan Badawi, Umit Y. Ogras |
|---|---|
| Primary category | cs.AR |
| Published | 12 August 2026 |
| Last updated at source | 12 August 2026 |
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