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arXiv · 2610.21378

DPA-MoE: A Runtime Precision Control Framework for MoE Inference

Jaeyun Kim·Hoonki Lee·Susie Shin·Hyunmin Kim·Dahyun Lee·Sangyeob Kim
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Abstract

Mixture-of-Experts (MoE) models reduce computation by routing each token to a small set of experts, but each selected expert is still used at a fixed precision. We present DPA-MoE, a runtime precision control framework that makes routed experts any-precision. DPA-MoE encodes expert feed-forward weights as prefix-decodable chunks using SharedScale AnyBCQ, profiles per-expert reconstruction residuals offline, and allocates chunks at inference time using live router gate scores. This post-routing allocation lets a single encoded MoE checkpoint operate at multiple effective weight budgets without retraining or re-encoding. Experiments on Qwen3-30B-A3B and DeepSeekMoE-16B show that DPA-MoE improves KL divergence and downstream accuracy over uniform chunk allocation at matched budgets, with modest steady-state throughput overhead in our prototype.

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Jaeyun Kim, Hoonki Lee, Susie Shin, Hyunmin Kim, Dahyun Lee, Sangyeob Kim. 2026-10-15. DPA-MoE: A Runtime Precision Control Framework for MoE Inference. https://arxiv.org/abs/2610.21378

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