Why MoE Models Benefit More from Speculative Decoding
Original: Why MoE models get more from speculative decoding Apr 21, 2026 9 min read
Cohere explains why MoE architecture can amplify speculative decoding speedups through sparsity, expert overlap, and low-batch overhead amortization.
Cohere analyzes why speculative decoding behaves differently on Mixture-of-Experts models than on dense LLMs. Its benchmarks show MoE speedups can peak at moderate batch sizes because sparse expert routing keeps verification bandwidth-bound. The post also finds that temporal expert overlap and fixed overhead amortization make multi-token verification cheaper than simple worst-case models predict.
Cohere’s technical post examines why Mixture-of-Experts models can gain unusually strong benefits from speculative decoding, a serving technique where a smaller draft model proposes several future tokens and a larger target model verifies them in one forward pass. The core question is whether MoE routing, which may require loading different experts for different tokens, undermines the expected speedup. Cohere’s conclusion is that MoE sparsity can actually make speculative decoding more effective, but the effect depends strongly on batch size and routing behavior.
Free shows the 3-line summary; Pro unlocks the full deep summary (~300 words) so you never have to click through.
See Pro plans →Want the original English / full article?
Read on Cohere Blog →Summaries are AI-generated; the original article is authoritative.