OrderMoE: An expert similarity driven distributed edge MoE inference
Although mixture-of-experts, MoE, models have been increasingly adopted to scale large language models with moderate computation cost, it remains challenging to deploy MoE inference over resource-constrained and bandwidth-limited edge infrastructures. Existing distributed MoE serving methods mainly rely on exact expert placement, caching, replication, or communication scheduling, while overlooking the functional similarity among experts, which provides an opportunity to reduce cross-server token transmission. Therefore, this paper introduces a similarity-aware expert allocation and distributed deployment framework, dubbed OrderMoE, which aims to accelerate edge MoE inference while balanci...
| Authors | Xin Yuan, Ning Li, Quan Chen et al. |
|---|---|
| Primary category | cs.NI |
| Published | 19 July 2026 |
| Last updated at source | 12 August 2026 |
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