<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Vllm on Caktus Group</title><link>https://www.caktusgroup.com/tags/vllm/</link><description>Recent content in Vllm on Caktus Group</description><generator>Hugo</generator><language>en</language><lastBuildDate>Fri, 30 Oct 2026 09:00:00 -0400</lastBuildDate><atom:link href="https://www.caktusgroup.com/tags/vllm/index.xml" rel="self" type="application/rss+xml"/><item><title>How Caktus Runs and Uses Local Models</title><link>https://www.caktusgroup.com/blog/2026/10/30/how-caktus-runs-and-uses-local-models/</link><pubDate>Fri, 30 Oct 2026 09:00:00 -0400</pubDate><guid>https://www.caktusgroup.com/blog/2026/10/30/how-caktus-runs-and-uses-local-models/</guid><description>&lt;p>As a research project this year at Caktus, we&amp;rsquo;ve been experimenting with serving open-weight language models on our own hardware. In this post, we&amp;rsquo;ll walk through the hardware, the Docker Compose stack that serves our models, the Gemma and Qwen configurations we tested, our benchmarks, and the configurations we use in &lt;a href="https://pi.dev" target="_blank" rel="noopener noreferrer">pi.dev&lt;/a> and &lt;a href="https://code.visualstudio.com/docs/agent-customization/language-models" target="_blank" rel="noopener noreferrer">VS Code&lt;/a>.&lt;/p></description></item></channel></rss>