<?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>推理加速 on 硅基 AGI · 智能体学习与测评</title><link>https://guijiagi.com/tags/%E6%8E%A8%E7%90%86%E5%8A%A0%E9%80%9F/</link><description>Recent content in 推理加速 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 11:33:00 +0800</lastBuildDate><atom:link href="https://guijiagi.com/tags/%E6%8E%A8%E7%90%86%E5%8A%A0%E9%80%9F/index.xml" rel="self" type="application/rss+xml"/><item><title>AI推理加速：Flash Attention原理与实现</title><link>https://guijiagi.com/posts/b1-38dc2b9b/</link><pubDate>Thu, 16 Jul 2026 11:33:00 +0800</pubDate><guid>https://guijiagi.com/posts/b1-38dc2b9b/</guid><description>深入解析Flash Attention的算法原理、GPU内存层次优化与实际性能表现</description></item><item><title>大模型推理的Speculative 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+0800</pubDate><guid>https://guijiagi.com/posts/llm-inference-acceleration-2026-vllm-sglang-tensorrt/</guid><description>三大推理引擎全面对比：vLLM、SGLang、TensorRT-LLM在2026年的性能与功能评测</description></item><item><title>大模型推理加速技术全景</title><link>https://guijiagi.com/posts/llm-inference-acceleration-2026/</link><pubDate>Sat, 27 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/llm-inference-acceleration-2026/</guid><description>大模型推理加速技术全景解析，从KV Cache到投机解码的完整技术栈</description></item><item><title>大模型推理加速技术全景</title><link>https://guijiagi.com/posts/llm-inference-acceleration-survey/</link><pubDate>Sat, 27 Jun 2026 15:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/llm-inference-acceleration-survey/</guid><description>大模型推理加速技术全景</description></item><item><title>投机解码深度解析：LLM 推理速度翻倍的秘密</title><link>https://guijiagi.com/posts/speculative-decoding-explained/</link><pubDate>Thu, 25 Jun 2026 12:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/speculative-decoding-explained/</guid><description>从 Speculative Decoding 到 Medusa 和 EAGLE，全面解析投机解码如何在不损失质量的前提下将 LLM 推理速度提升 2-4 倍。</description></item><item><title>投机解码深度解析：让 LLM 推理快 3 倍</title><link>https://guijiagi.com/posts/speculative-decoding-deep/</link><pubDate>Thu, 25 Jun 2026 10:00:00 +0800</pubDate><guid>https://guijiagi.com/posts/speculative-decoding-deep/</guid><description>深入解析投机解码原理、Medusa/EAGLE/Lookahead 等加速方案</description></item></channel></rss>