返回全部动态
NVIDIA 发布投机解码指南，加速 LLM 推理
原标题：Co-Designing AI Models Using Speculative Decoding for Faster LLM Inference
AI 摘要
NVIDIA 技术博客发布系列文章第三篇，探讨通过投机解码加速 LLM 推理同时保持准确性。文章提出五条指南，用于在帕累托前沿选择草稿长度和机制，并分析了草稿长度对 GEMM 计算强度、注意力内核利用率及 KV 缓存压力的影响。
以上摘要由 AI 生成，可能存在误差。事实请以原文为准。
正文节选
This post is the third in a series on AI model co-design. It explores how to accelerate LLM inference while maintaining accuracy using speculative decoding and offers five guidelines for selecting draft length and draft mechanism across the Pareto frontier. For a discussion of how model design choices impact both throughput and interactivity without sacrificing accuracy, see AI Model Co-Design: Hardware-Friendly LLM Design (Part 1). For an explanation of how group size (query heads per KV head),
发布时间：2026-09-03 00:04
抓取时间：2026-09-03 00:13
来源机构：NVIDIA