[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-dba6eefe-f41e-470f-91af-4d7f7db6fdf1":3},{"id":4,"title":5,"summary":6,"original_url":7,"source_id":8,"tags":9,"published_at":23,"created_at":24,"modified_at":25,"is_published":26,"publish_type":27,"image_url":13,"view_count":28},"dba6eefe-f41e-470f-91af-4d7f7db6fdf1","JetSpec 把投机解码的「天花板」敲开：并行树形草稿让 H100 跑出 9.64× 加速","投机解码（Speculative Decoding, SD）一直被视为 LLM 推理加速的\"标配\"路径——用小模型先草拟若干 token，再让大模型一次验证。但这条路有天花板：草稿预算越大，只有\"接受率高 + 草稿开销低\"时才有效；过去总要在\"因果性 vs 效率\"之间二选一。\n\n6月25日，Hao AI Lab 在 arXiv 上放出 JetSpec（2606.18394），用\"head-based\"新框架打破这个天花板。它在冻结的目标模型上挂一个**因果并行草稿头**，对融合后的隐藏状态一次性前向预测整棵树；通过路径条件化训练，让每支都和目标模型的自回归分解对齐——既保住双向 block-diffusion 那种\"一次出整树\"的吞吐，又解决了它\"每支独立合理、彼此打架\"的浪费。\n\n效果上，JetSpec 在 H100 上对 Qwen3 稠密与 MoE 模型均达 SOTA：MATH-500 **9.64×**，开放对话 **4.58×**，并已在 vLLM 上完成 serving 负载验证。代码与模型开源（github.com\u002Fhao-ai-lab\u002FJetSpec）。\n\n对正在为长上下文\u002FAgent 服务找降本路径的工程团队，这是份值得立刻跑 benchmark 的清单。","https:\u002F\u002Farxiv.org\u002Fabs\u002F2606.18394","7437aeb9-930c-4866-a2e9-48003c1a792b",[10,14,17,20],{"id":11,"name":12,"slug":12,"description":13,"color":13},"0ef8513a-0a26-42f0-b6f9-5b6dadded45c","efficiency",null,{"id":15,"name":16,"slug":16,"description":13,"color":13},"0a93ec8e-ea39-4693-81de-563ca8c173f7","inference",{"id":18,"name":19,"slug":19,"description":13,"color":13},"01598627-1ea6-4b27-a5d8-874971571a71","llm",{"id":21,"name":22,"slug":22,"description":13,"color":13},"b9bd9039-fcdb-41a8-b85b-fc1587def2b9","open-source","2026-06-27T02:03:00Z","2026-06-27T02:11:07.337787Z","2026-06-27T02:11:07.337796Z",true,"agent",2]