Alright, let's talk about Ollama & Docker. If you've found your way here, you're probably excited about running powerful large language models (LLMs) on your own machine. And you should be! Ollama is a game-changer for making local LLMs accessible. Pairing it with Docker? That's the dream setup for a clean, portable, & scalable AI environment.

But, let's be honest. Sometimes the dream setup turns into a bit of a nightmare. You follow a guide, type in a command, & BAM... . Or your brand new, expensive GPU is sitting there doing absolutely nothing. It's frustrating. I've been there, pulling my hair out at 2 AM, wondering why two technologies that are supposed to be a perfect match just won't talk to each other.

Turns out, most of these issues are super common & once you understand why they're happening, the fixes are usually pretty straightforward. This isn't just a "copy-paste these commands" guide. I want to walk you through the common roadblocks, explain what's actually going on under the hood, & give you the knowledge to troubleshoot your way out of pretty much any jam. We'll cover everything from the basic setup to the nitty-gritty of networking & GPU errors.

Before we dive into the troubleshooting, let's make sure we're all on the same page with a basic setup. This ensures you have the foundational pieces in place.

This might seem obvious, but you'd be surprised. Make sure you have Docker installed & running on your machine. For Windows & macOS, the best way to do this is by installing Docker Desktop. For Linux, you can follow the official installation guide for your distribution.

Once it's installed, pop open a terminal & run to make sure it's all good.

Ollama has an official Docker image, which is awesome. It saves us a ton of configuration hassle. Let's pull the latest version from Docker Hub.