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arXiv Machine Learning By Mengqi Guo, Zhe Wei, Yuan Yuan, Jiunn Bin Lim, Boyi Pan, Michael Bi Mi

DuoRate: Two-Rate Per-Expert Precision Allocation for Mixture-of-Experts Serving

Read the original on arXiv Machine Learning →

The paper introduces DuoRate, a method for allocating precision in Mixture-of-Experts (MoE) models that separates storage and decoding rates. It stores a uniform tier of weights once and then adapts read precision per workload using a Lagrangian rate‑distortion optimization, reducing the store‑read gap. Experiments on Qwen3-30B-A3B and GSM8K show that DuoRate achieves higher accuracy at lower bit‑per‑weight (bpw) rates compared to fixed‑format baselines.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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