论文揭示了MoE模型中三种推理优化为何实际效果不佳，对模型部署有重要参考价值。
研究测量了OLMoE-1B-7B、DeepSeek-V2-Lite和Qwen3-30B-A3B三种MoE模型上的三种推理优化效果。Fused Triton内核在隔离测试中达到5.6x至9.0x加速，但端到端实际效果仅为0.999x，远低于1.07x的理论上限。INT4量化平均每位置仅改变8个专家中的0.53个，且通过全精度权重重放这些改变路线仅造成2.7%的质量损失。
Launch-Bound and Substitutable: Why Three Inference Optimizations Fail to Pay Off in Mixture-of-Experts Models
Mixture-of-Experts (MoE) models route each token to a few of many expert networks, and that routing is data- dependent in a way standard inference optimizations do not ex- pect. This paper measures what three of them actually deliver on OLMoE-1B-7B, DeepSeek-V2-Lite, and Qwen3-30B-A3B. Fused Triton kernels reach 5.6x to 9.0x in isolation but 0.999x end to end against a measured 1.07x ceiling, because the model spends its time waiting on roughly a thousand kernel launches per forward pass rather than on the arithmetic those kernels improve. INT4 quantization changes on average 0.53 of the eight selected experts per token position, yet replaying exactly those changed routes through full-precision weights reproduces only 2.7% of the quality loss, which makes the experts substitutable rather than specialized. Removing all 23 torch.compile graph breaks, the step prior work treats as the structural fix, makes the model three times slower. A fourth result ties the three together: leaving the routers in FP16 lowers drift by 20% while raising loss, so routing fidelity and output quality are separable objectives. Every number recomputes from committed per-token route dumps.