Unverified50% confidenceOpinionExact time
The end-to-end speedup of MoE speculative decoding depends on two factors: the acceptance rate of draft tokens and the degree of expert overlap within the draft window.
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Long-term
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10/29/2026
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VerifiedA higher acceptance rate means the draft model's guesses are more accurate and the large model needs to redo less work, which directly determines the actual speedup achieved by speculative decoding.76% similarUnverifiedSpeculative decoding can achieve several-fold improvements in inference throughput when the draft acceptance rate is high.72% similarUnverified投机采样的理论加速上限由草稿模型的命中率决定，在创意写作、多语言等分布复杂场景下命中率会显著下降69% similarUnverifiedIn code benchmarks such as SWE-bench, optimizations at the framework level have an even greater impact on final scores than differences in the models' own capabilities.68% similarUnverifiedReview capacity must scale at the same rate as writing capacity; if review does not run in parallel, output equals zero. This is the most important judgment of this model: the real bottleneck is not writing, it is review.68% similar
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