<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Raspberry Pi on funinkina's corner</title><link>https://funinkina.co.in/tags/raspberry-pi/</link><description>Recent content in Raspberry Pi on funinkina's corner</description><generator>Hugo</generator><language>en</language><lastBuildDate>Fri, 31 Jan 2025 23:42:36 +0530</lastBuildDate><atom:link href="https://funinkina.co.in/tags/raspberry-pi/index.xml" rel="self" type="application/rss+xml"/><item><title>Running Deepseek on Raspberry Pi</title><link>https://funinkina.co.in/blog/running-deepseek-on-raspberry-pi/</link><pubDate>Fri, 31 Jan 2025 23:42:36 +0530</pubDate><guid>https://funinkina.co.in/blog/running-deepseek-on-raspberry-pi/</guid><description>&lt;p&gt;Let&amp;rsquo;s start by addressing the elephant in the room, why? Why would I run a freaking Large Language Model on a 4-core arm processor with 4GB of RAM? Well, why not? I have a Raspberry Pi 4 lying around and I wanted to see if I could run Deepseek on it. (Also cause free content for the blog). Before starting, let me say, I have absolutely zero expectations from this experiment. I am not expecting it to work, I am not expecting it to be fast, I am not expecting it to be usable. I am just doing it because I can. So, let&amp;rsquo;s get started.&lt;/p&gt;</description></item></channel></rss>