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AI CURATED HOT STORY · 11/3/2026

Cohere Paper Validates Speculative Decoding for Low-Batch MoE Models

1 reports archived1 independent sourcesupdated 11/3/2026, 05:18:34
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A new Cohere paper challenges the assumption that speculative decoding yields minimal gains for Mixture-of-Experts (MoE) models at low batch sizes. The study reveals that adjacent tokens share 20-45% of their experts—far exceeding theoretical predictions—making verification steps highly efficient. On models like Qwen3-235B, verification runs at 89% of normal decoding speed, offering a viable optimization path for agent workloads.

LATEST/A new Cohere paper challenges the assumption that speculative decoding yields minimal gains for Mixture-of-Experts (MoE) models at low batch sizes. The study reveals that adjacent tokens share 20-45% of their experts—far exceeding theoretical predictions—making verification steps highly efficient. On models like Qwen3-235B, verification runs at 89% of normal decoding speed, offering a viable optimization path for agent workloads.

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  1. Rohan Paul (@rohanpaul_ai)T2·78 pts

    Cohere Paper Validates Speculative Decoding for Low-Batch MoE Models

    • Adjacent tokens in MoE models actually share 20-45% of experts, significantly higher than the 4-12% predicted by independent routing assumptions
    • High expert reuse and efficient bandwidth utilization allow speculative decoding to minimize verification overhead even at Batch Size 1
    • Benchmarks on Qwen3-235B FP8 show verification runs at 89% of normal decoding speed, proving its utility in low-batch scenarios