<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Offline on AI VOID</title><link>https://ai-blog.noorshomelab.dev/tags/offline/</link><description>Recent content in Offline on AI VOID</description><generator>Hugo</generator><language>en</language><lastBuildDate>Tue, 30 Dec 2025 00:00:00 +0000</lastBuildDate><atom:link href="https://ai-blog.noorshomelab.dev/tags/offline/index.xml" rel="self" type="application/rss+xml"/><item><title>Local LLMs with any-llm (Ollama Integration)</title><link>https://ai-blog.noorshomelab.dev/any-llm-guide-2025/local-llms-ollama/</link><pubDate>Tue, 30 Dec 2025 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/any-llm-guide-2025/local-llms-ollama/</guid><description>&lt;h2 id="introduction-bringing-llms-home"&gt;Introduction: Bringing LLMs Home&lt;/h2&gt;
&lt;p&gt;Welcome back, future AI architect! So far in our &lt;code&gt;any-llm&lt;/code&gt; journey, we&amp;rsquo;ve largely focused on interacting with powerful cloud-based LLMs like OpenAI, Anthropic, or Mistral. These services are incredible for their scale and performance, but what if you need more privacy, lower latency, or simply want to experiment without incurring API costs?&lt;/p&gt;
&lt;p&gt;This chapter is all about bringing the power of Large Language Models directly to your machine. We&amp;rsquo;ll dive into the exciting world of &lt;strong&gt;Local LLMs&lt;/strong&gt; and learn how to run them efficiently using a fantastic tool called &lt;strong&gt;Ollama&lt;/strong&gt;. Best of all, we&amp;rsquo;ll see how &lt;code&gt;any-llm&lt;/code&gt; seamlessly integrates with Ollama, allowing you to switch between local and cloud models with minimal code changes. Pretty neat, right?&lt;/p&gt;</description></item></channel></rss>