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About this paper
Jinwei Kong, Runqi Meng, Fanyi Wang, Wentao Qiu, Haotian Hu, Yongjian Zhou, Zhenhua Ge
SpecPrefetch offers a parameter-efficient framework for sparse Mixture of Experts (MoE) models that reduces inference latency by accurately predicting and asynchronously prefetching next-layer experts. It achieves up to an 8.12% increase in decoding throughput on mobile platforms, particularly benefiting storage-constrained scenarios, while preserving the original model's routing decisions.
Key insights
- Accurate prediction of next-layer expert demands is achievable well in advance using lightweight, layer-specific adapters, without altering the native MoE model's routing or requiring its retraining.
- Effective prefetching necessitates a window-aware budgeting policy, as blindly increasing prediction accuracy or candidate sets can exceed finite transfer capacity and degrade overall performance.
- Decoupling the prefetching mechanism from the original model's routing ensures that performance optimizations do not compromise model output fidelity, allowing for seamless integration with pretrained MoE models.
Summary by alphaXiv.
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