<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Triton on PlumePHP</title><link>https://plumephp.com/tags/triton/</link><description>Recent content in Triton 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/triton/index.xml" rel="self" type="application/rss+xml"/><item><title>NVIDIA Triton Inference Server 生产部署</title><link>https://plumephp.com/ai-triton-server/</link><pubDate>Thu, 10 Sep 2026 10:00:00 +0800</pubDate><guid>https://plumephp.com/ai-triton-server/</guid><description>&lt;p&gt;在 AI 模型落地的最后一公里，推理服务的稳定性、吞吐量和延迟直接决定了用户体验。NVIDIA Triton Inference Server（以下简称 Triton）作为企业级推理框架的代表，已经成为大型生产系统的标配组件。本文将从架构原理、配置调优到生产部署实践，系统梳理 Triton 的完整使用路径。&lt;/p&gt;</description></item></channel></rss>