Federation of Experts: Communication Efficient Distributed Inference for Large Language Models

Abstract:Mixture of experts has emerged as the primary mechanism for making Large Language Models (LLMs) computationally efficient. However, in distributed settings, communicating token embeddings between experts is a significant bottleneck. We present the novel Federation of Experts (FoE) architecture. FoE restructures the MoE block of a transformer layer into multiple MoE clusters. Each cluster is responsible for only one of the KV heads and expert parallelism is applied between those experts. Between clusters, a sum synchronizes the post-attention residuals, which then drives routing and dispatch for the next MoE block. In a single-node setting, FoE completely eliminates all-to-all communication as all experts within a group are contained on the same GPU. In multi-node settings, FoE confines all-to-all communication to the intra-node fabric, thus significantly reducing communication overhead. An implementation of FoE finds that on LongBench, FoE significantly improves inference throughput and latency in both single-node and multi-node settings, reducing end-to-end prefill latency by up to 5.88x, TTFT by 3.66x, and TPOT by 1.53x. It does so while achieving comparable generation quality to a mixture of experts model of the same size and training configuration.

Machine Learning (cs.LG)

arXiv:2605.06206 [cs.LG]

(or arXiv:2605.06206v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2605.06206 Focus to learn more arXiv-issued DOI via DataCite

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