<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>边缘部署 on PlumePHP</title><link>https://plumephp.com/tags/%E8%BE%B9%E7%BC%98%E9%83%A8%E7%BD%B2/</link><description>Recent content in 边缘部署 on PlumePHP</description><generator>Hugo</generator><language>zh-CN</language><lastBuildDate>Thu, 10 Sep 2026 10:00:00 +0800</lastBuildDate><atom:link href="https://plumephp.com/tags/%E8%BE%B9%E7%BC%98%E9%83%A8%E7%BD%B2/index.xml" rel="self" type="application/rss+xml"/><item><title>模型剪枝与知识蒸馏：从压缩到加速全链路</title><link>https://plumephp.com/ai-model-compression/</link><pubDate>Thu, 10 Sep 2026 10:00:00 +0800</pubDate><guid>https://plumephp.com/ai-model-compression/</guid><description>&lt;h2 id="引言为什么需要模型压缩"&gt;引言：为什么需要模型压缩&lt;/h2&gt;
&lt;p&gt;深度学习模型的参数量正在以指数级增长。从 AlexNet 的 6000 万参数，到 GPT-4 的万亿级参数，模型能力的提升往往伴随着体积的膨胀。然而，在生产环境中部署这些庞大模型时，我们面临着严峻的现实约束：&lt;/p&gt;</description></item></channel></rss>