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arXiv · 2607.00466

ELDR: Expert-Locality-Aware Decode Routing for PD-Disaggregated MoE Serving

Sangjin Choi·Sukmin Cho·Yifan Xiong·Ziyue Yang·Youngjin Kwon·Peng Cheng
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Abstract

In prefill-decode (PD) disaggregated LLM serving, each request is assigned to a decode worker after prefill. Existing decode routers balance only load; for mixture-of-experts (MoE) models this is incomplete: equally loaded workers can differ in latency, since each decode step loads the weights of every distinct expert its batch activates. We present ELDR, an expert-locality-aware decode router for PD-disaggregated MoE serving. From a request's prefill expert activations, ELDR builds an expert signature predicting the experts it will activate during generation. Offline, balanced K-means partitions signature space across decode workers; online, locality-band routing sends each request to the least-loaded worker among those best matching its signature. A signature cache, co-indexed with the KV cache at KV-block granularity, keeps signatures exact under prefix caching. Implemented in vLLM and evaluated on deployments of up to 40 GPUs, ELDR reduces median TPOT by 5.9-13.9% over the strongest of four load-balancing baselines across three MoE models and two workloads, with model outputs unchanged.

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Sangjin Choi, Sukmin Cho, Yifan Xiong, Ziyue Yang, Youngjin Kwon, Peng Cheng. 2026-07-02. ELDR: Expert-Locality-Aware Decode Routing for PD-Disaggregated MoE Serving. https://arxiv.org/abs/2607.00466

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