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English(EN) JetSpec++: Foresight Parallel Drafting with Self Refinements

JetSpec++论文介绍通过推测性解码加速LLM推理

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了JetSpec++，这是一种新颖的推测性解码方法，可显著提高大型语言模型的速度。通过采用块内自精炼和块间前瞻性草稿，JetSpec++在传统自回归解码方面可实现高达7.45倍的速度提升。该方法允许在当前块完全验证之前草稿新令牌块，从而有效地重叠草稿和验证阶段以最小化实际运行时间成本。在GB200硬件上进行测试，JetSpec++在各种基准测试中实现了每秒1,361个令牌的吞吐量，平均接受长度接近九个令牌，同时保持目标模型的输出分布。 AI

影响 加速LLM推理速度，可能实现更具响应性和效率的AI应用。

排序理由 该集群包含一篇研究论文，详细介绍了提高LLM推理速度的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

JetSpec++论文介绍通过推测性解码加速LLM推理

本文如何被排名

Signal score
16 / 100
Composite score across the factors below. Higher = stronger signal that this story matters right now.
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Tool
该集群包含一篇研究论文，详细介绍了提高LLM推理速度的新方法。[lever_c_demoted from research: ic=1 ai=1.0]
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Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics
paper, infra
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High
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Breaking (< 6h)
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完整方法见我们的编辑标准。

报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · Zhaoxiang Feng (UC San Diego), Lanxiang Hu (UC San Diego), Haoran Yuan (UIUC), Yu-Yang Qian (Nanjing University), Jihui Yang (Meta), Peng Zhao (Nanjing University), Yibo Zhu (StepFun), Tajana Rosing (UC San Diego), Hao Zhang (UC San Diego) ·

    JetSpec++：具有自我完善的前瞻性并行草稿

    arXiv:2610.19792v1 Announce Type: new Abstract: Speculative decoding's effectiveness is governed by two quantities: the acceptance rate of drafted tokens and the cost of producing them. We show the two objectives can be co-optimized with algorithm-system co-design, and present Je…

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