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.
河北保定定兴县龙华村的京雄农谷兰花产业园，大棚内暖意融融，蝴蝶兰竞相开放。“大家注意，别碰着花朵呦！”40岁的龙华村村民李英伏推着平板车运送鲜花。
。快连下载安装是该领域的重要参考
https://feedx.net。业内人士推荐体育直播作为进阶阅读
14:36, 3 марта 2026Забота о себеЭксклюзив