SpecPrefech: Efficient Expert Prefetching for MoE Models
A recent study published on arXiv introduces SpecPrefetch, an efficient framework designed for prefetching expert weights in sparse Mixture-of-Experts (MoE) foundation models. While MoE models enhance their capacity via conditional expert activation, they encounter deployment issues when accelerator memory is restricted due to expert offloading. The bottleneck caused by routing-dependent transfers leads to the serialization of routing, expert loading, and execution processes. SpecPrefetch employs a shared, lightweight adapter to anticipate next-layer expert candidates for asynchronous transfers, while the native router, which remains unchanged, identifies the experts to be executed. This approach minimizes the latency associated with expert loading without modifying the original routing mechanism.
Key facts
- Sparse MoE models use conditional expert activation to expand capacity.
- Expert offloading moves inactive experts to host memory or storage.
- Routing-dependent transfer bottleneck serializes routing, expert loading, and execution.
- SpecPrefetch uses a shared lightweight adapter for next-layer expert prediction.
- The frozen native router determines final executed experts.
- SpecPrefetch separates transfer prediction from execution routing.
- It reduces exposed expert-loading latency.
- The paper is on arXiv with ID 2607.24787.
Entities
Institutions
- arXiv