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arXiv · 2607.23099

Decoding the Skew: Distribution-Aware MoE Inference with Adaptive Kernel Dispatch

En-Ming Huang·An-Cheng Chang·Bai-Cheng Jeng·Shih-Hao Hung·H. T. Kung
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Abstract

Mixture-of-Experts (MoE) inference consists of sparse expert GEMMs whose shapes vary with the runtime routing distribution. Existing serving systems typically select fused-MoE kernels using static token-count buckets, ignoring the per-expert routing distribution that determines tile padding, memory reuse, and kernel efficiency. We introduce a distribution-aware framework for modeling and benchmarking MoE inference. The framework combines the compact Effective Experts metric with a Dirichlet-based reverse-modeling procedure that generates controllable routing distributions for systematic hardware studies. Using it, we show that the best fused-MoE kernel changes with routing skew and token count. We further present DA-MoE, a GPU-resident kernel-dispatch runtime for NVIDIA GPUs that matches the live routing histogram to offline-tuned distributions and selects a near-optimal fused-MoE kernel without CPU--GPU synchronization. On HumanEval-X serving traces, DA-MoE improves geomean fused-MoE latency by 1.16X on DeepSeek-V3 and 1.29X on Kimi K2, with peak speedups of 1.40X and 1.56X.

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En-Ming Huang, An-Cheng Chang, Bai-Cheng Jeng, Shih-Hao Hung, H. T. Kung. 2026-08-21. Decoding the Skew: Distribution-Aware MoE Inference with Adaptive Kernel Dispatch. https://arxiv.org/abs/2607.23099

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