<rss xmlns:atom="http://www.w3.org/2005/Atom" version="2.0"><channel><title>Llm - Tag - David Woods</title><link>https://dwooods.github.io/tags/llm/</link><description>Llm - Tag - David Woods</description><generator>Hugo -- gohugo.io</generator><language>en</language><copyright>David Woods</copyright><lastBuildDate>Mon, 21 Sep 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://dwooods.github.io/tags/llm/" rel="self" type="application/rss+xml"/><item><title>The Obvious Answer Was Wrong (Local LLMs on a PC and a Pi)</title><link>https://dwooods.github.io/posts/local-llms-pc-and-pi/</link><pubDate>Mon, 21 Sep 2026 00:00:00 +0000</pubDate><author>David Woods</author><guid>https://dwooods.github.io/posts/local-llms-pc-and-pi/</guid><description><![CDATA[<div class="featured-image">
                <img src="/images/hero-pc-vs-pi.jpeg" referrerpolicy="no-referrer">
            </div>I benchmarked open-weight LLMs on a Windows PC and a Raspberry Pi 5, and nearly every obvious answer turned out wrong: a GPU silently running on CPU, a &lsquo;bigger&rsquo; model slower than no GPU at all, a judge model that graded a correct answer wrong. Local earned its place for narrow, lightweight work. It isn&rsquo;t yet a replacement for the closed models I use every day, and I didn&rsquo;t put them through the same tests.]]></description></item></channel></rss>