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arXiv · 2607.17880

Mixture-of-Experts Serving

Zhiyi Huang·Qinpei Lou·Tao Xiao
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Abstract

Mixture-of-Experts (MoE) models route each token to only a few expert networks, distributing the serving load across experts whose popularity shifts over time. A serving system must therefore dynamically decide how many GPUs to assign to each expert, trading off service latency against the cost of reconfiguring the assignment. We introduce a formal model of MoE Serving and initiate a principled study of online and offline algorithms for it. Our main result is a polynomial-time $O(\sqrt{\log k})$-competitive online algorithm, where $k$ is the number of GPUs beyond one per expert. We complement it with a matching $\Omega(\sqrt{\log k})$ barrier for the online dual problem underlying our analysis. In the offline setting, we give a constant-factor approximation, show that MoE Serving is NP-hard, and rule out an FPTAS assuming ETH.

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Zhiyi Huang, Qinpei Lou, Tao Xiao. 2026-07-20. Mixture-of-Experts Serving. https://arxiv.org/abs/2607.17880

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