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.
Dify 构建 FE 工作流：前端团队可复用 AI 工作流实战
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Известная телеведущая пожаловалась на сексуальные домогательства на работе20:33
Виктория Кондратьева (Редактор отдела «Мир»)。关于这个话题，safew官方下载提供了深入分析
2025年，广大政协委员积极发挥在相关专业领域代表性强、联系面广、影响力大的优势，深入基层、深入界别群众，当好党的政策宣传者、群众利益维护者、社会和谐促进者，得到群众充分肯定和广泛好评——。关于这个话题，一键获取谷歌浏览器下载提供了深入分析
shouldn’t have backward compatibility issues.