<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:sy="http://purl.org/rss/1.0/modules/syndication/" xmlns:media="http://search.yahoo.com/mrss/"><channel><title>Triton on Shaowen Chen's Website</title><link>https://www.chenshaowen.com/en/tags/triton/</link><description>Recent content in Triton on Shaowen Chen's Website</description><generator>Hugo -- gohugo.io</generator><language>en</language><copyright>&amp;copy;2016 - {year}, All Rights Reserved.</copyright><lastBuildDate>Sat, 03 Feb 2024 08:05:48 +0000</lastBuildDate><sy:updatePeriod>weekly</sy:updatePeriod><atom:link href="https://www.chenshaowen.com/en/tags/triton/atom.xml" rel="self" type="application/rss+xml"/><item><title>Large Model Inference with Triton Server and TensorRT-LLM in a Container</title><link>https://www.chenshaowen.com/en/blog/using-triton-server-and-tensorrt-llm-under-container.html</link><pubDate>Sat, 03 Feb 2024 08:05:48 +0000</pubDate><atom:modified>Sat, 03 Feb 2024 08:05:48 +0000</atom:modified><guid>https://www.chenshaowen.com/en/blog/using-triton-server-and-tensorrt-llm-under-container.html</guid><description>1. Compiling Models with TensorRT-LLM 1.1 Introduction to TensorRT-LLM When using TensorRT, you usually need to convert the model to ONNX format, then convert the ONNX to TensorRT format, and finally run inference in TensorRT or Triton Server. But this conversion process is not simple, and you often run into</description><dc:creator>微信公众号</dc:creator><category>AI</category><category>Triton</category><category>TensorRT</category><category>LLM</category><category>Inference</category><category>GPU</category><category>Operations</category><category>Containers</category><category>Learning</category></item></channel></rss>