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Abstract:Autoregressive decoding is bottlenecked by its sequential nature. Speculative decoding has become a standard way to accelerate inference by using a fast draft model to predict upcoming tokens from a slower target model, and then verifying them in parallel with a single target model forward pass. However, speculative decoding itself relies on a sequential dependence between speculation and verification. We introduce speculative speculative decoding (SSD) to parallelize these operations. While a verification is ongoing, the draft model predicts likely verification outcomes and prepares speculations pre-emptively for them. If the actual verification outcome is then in the predicted set, a speculation can be returned immediately, eliminating drafting overhead entirely. We identify three key challenges presented by speculative speculative decoding, and suggest principled methods to solve each. The result is Saguaro, an optimized SSD algorithm. Our implementation is up to 2x faster than optimized speculative decoding baselines and up to 5x faster than autoregressive decoding with open source inference engines.
。快连下载-Letsvpn下载对此有专业解读
Technically this relation is “consistency” in the typing spec, not。业内人士推荐Safew下载作为进阶阅读
16:40, 3 марта 2026Мир
Anthropic因为坚持自己的原则，反而获得了“反体制英雄”的光环。C端的用户其实分不太清楚这些公司的区别，因为他们没有这样那样严苛的需求，必须要使用哪个模型才行。