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

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.

Computation and Language (cs.CL)

arXiv:2610.21378 [cs.CL]

(or arXiv:2610.21378v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2610.21378 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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