TopoCompress: Topology Aware Token Compression Algorithm for Distributed Edge MoE Inference
Mixture-of-experts (MoE) models improve capacity with moderate overhead by sparsely activating experts per token. However, deploying MoE across resource-constrained edge servers incurs substantial cross-server communication as experts are distributed across heterogeneous servers. Existing placement methods optimize for raw token traffic, while conventional compression considers semantics but ignores topology-dependent routing costs. Consequently, independent optimization leads to inefficient communication and resource utilization. This paper proposes TopoCompress, a deployment- and topology-aware token compression framework for communication-efficient distributed edge MoE inference. It jo...
| Authors | Ning Li, Xinyu Wang, Xin Yuan et al. |
|---|---|
| Primary category | cs.NI |
| Published | 1 August 2026 |
| Last updated at source | 23 September 2026 |
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