TrimMoE A communication aware and adaptive depth framework for distributed edge inference
Serving Mixture-of-Experts (MoE) large language models across distributed edge servers is bottlenecked by the cross-server expert transmission. The existing approaches mainly focus on how to reach a remote expert faster. However, in this paper, we instead consider whether a given layer, and the layers after it, need to be executed at all. To this end, a communication-aware adaptive-depth framework is proposed in this paper, termed TrimMoE, which couples layer skipping and confidence-based early exit with substitute execution and server-expert selection under a unified quality budget. Specifically, in the offline stage, TrimMoE freezes the backbone, trains the lightweight per-layer exit he...
| Authors | Ning Li, Shuting Bai, Xin Yuan et al. |
|---|---|
| Primary category | cs.NI |
| Published | 1 August 2026 |
| Last updated at source | 12 August 2026 |
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