Enabling MoE on the Edge via Importance-Driven Expert Scheduling

• URL: http://arxiv.org/abs/2508.18983v1
• Date: Tue, 26 Aug 2025 12:32:09 GMT
• Title: Enabling MoE on the Edge via Importance-Driven Expert Scheduling
• Authors: Guoying Zhu, Meng Li, Haipeng Dai, Xuechen Liu, Weijun Wang, Keran Li, Jun xiao, Ligeng Chen, Wei Wang,
• Abstract summary: MoE is a key technique for scaling Large Language Models by activating only a subset of experts per query.<n>We leverage expert importance to guide decisions, substituting low-cached activated experts with functionally similar ones already cached in GPU memory.<n>This design reduces memory usage and data transfer, while largely eliminating PCIe overhead.
• Score: 21.860330824352527
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Mixture of Experts (MoE) architecture has emerged as a key technique for scaling Large Language Models by activating only a subset of experts per query. Deploying MoE on consumer-grade edge hardware, however, is constrained by limited device memory, making dynamic expert offloading essential. Unlike prior work that treats offloading purely as a scheduling problem, we leverage expert importance to guide decisions, substituting low-importance activated experts with functionally similar ones already cached in GPU memory, thereby preserving accuracy. As a result, this design reduces memory usage and data transfer, while largely eliminating PCIe overhead. In addition, we introduce a scheduling policy that maximizes the reuse ratio of GPU-cached experts, further boosting efficiency. Extensive evaluations show that our approach delivers 48% lower decoding latency with over 60% expert cache hit rate, while maintaining nearly lossless accuracy.

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