<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>推理优化 on 硅基 AGI · 智能体学习与测评</title><link>https://guijiagi.com/tags/%E6%8E%A8%E7%90%86%E4%BC%98%E5%8C%96/</link><description>Recent content in 推理优化 on 硅基 AGI · 智能体学习与测评</description><generator>Hugo</generator><language>zh-cn</language><copyright>本站内容采用 CC BY-NC-SA 4.0 国际许可协议授权</copyright><lastBuildDate>Thu, 16 Jul 2026 11:01:00 +0800</lastBuildDate><atom:link href="https://guijiagi.com/tags/%E6%8E%A8%E7%90%86%E4%BC%98%E5%8C%96/index.xml" rel="self" type="application/rss+xml"/><item><title>大模型推理优化全景：从KV Cache到投机解码</title><link>https://guijiagi.com/posts/b1-40a7d213/</link><pubDate>Thu, 16 Jul 2026 11:01:00 +0800</pubDate><guid>https://guijiagi.com/posts/b1-40a7d213/</guid><description>系统梳理大模型推理优化的核心技术路径，涵盖KV Cache、连续批处理、投机解码等前沿方案</description></item><item><title>大模型推理优化全景：从KV 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+0800</pubDate><guid>https://guijiagi.com/posts/article-82/</guid><description>深入解析Continuous Batching的原理、实现与工程优化，理解vLLM/TGI等推理框架的核心技术</description></item><item><title>大模型蒸馏技术：让小模型拥有大智慧</title><link>https://guijiagi.com/posts/article-49/</link><pubDate>Mon, 13 Jul 2026 01:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/article-49/</guid><description>从logit蒸馏到agent蒸馏，知识蒸馏技术的最新进展与实践指南</description></item><item><title>大模型蒸馏技术：让小模型拥有大智慧</title><link>https://guijiagi.com/posts/b2-7b5c9e69/</link><pubDate>Mon, 13 Jul 2026 01:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/b2-7b5c9e69/</guid><description>从logit蒸馏到agent蒸馏，知识蒸馏技术的最新进展与实践指南</description></item><item><title>大模型推理的KV Cache优化全解</title><link>https://guijiagi.com/posts/article-41/</link><pubDate>Sun, 12 Jul 2026 23:40:00 +0800</pubDate><guid>https://guijiagi.com/posts/article-41/</guid><description>从PagedAttention到多级缓存，KV Cache优化的技术全栈解析</description></item><item><title>大模型推理的KV 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+0800</pubDate><guid>https://guijiagi.com/posts/article-08/</guid><description>在统一硬件环境下，全面对比三种主流量化方案在推理速度、显存占用和模型质量上的表现</description></item><item><title>量化推理实战：AWQ vs GPTQ vs INT4性能对比</title><link>https://guijiagi.com/posts/b2-834c7318/</link><pubDate>Sun, 12 Jul 2026 18:10:00 +0800</pubDate><guid>https://guijiagi.com/posts/b2-834c7318/</guid><description>在统一硬件环境下，全面对比三种主流量化方案在推理速度、显存占用和模型质量上的表现</description></item><item><title>LLM负载均衡策略</title><link>https://guijiagi.com/posts/llm-load-balancing-strategy/</link><pubDate>Thu, 02 Jul 2026 11:23:00 +0800</pubDate><guid>https://guijiagi.com/posts/llm-load-balancing-strategy/</guid><description>从轮询到感知调度，LLM推理服务负载均衡的完整策略分析</description></item><item><title>连续批处理内部原理</title><link>https://guijiagi.com/posts/continuous-batching-internals/</link><pubDate>Thu, 02 Jul 2026 11:09:00 +0800</pubDate><guid>https://guijiagi.com/posts/continuous-batching-internals/</guid><description>解析vLLM连续批处理的内部机制，如何动态调度请求提升GPU利用率</description></item><item><title>KV 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