Setting Up My Own Private Coding Assistant with LLaMA 4

This week I have been playing with setting up my private LLM to help me program. I have heard a lot about the next-gen coding assistants that could/would take our developer jobs, and so I wanted to see if there was anything to it. After doing some research, I realized that running llama4 would require a TON of processing power, aka GPUs, and so I decided to rather run the actual LLM in a cluster on runpods, and simply access it from my local environment. Here are my steps

1. Create an account on runpods, and create (deploy) a pod with the at least 96GB VRAM and 4 GPUs. Make sure to expose port 11434 so that we can connect to it later from our local environment.
2. SSH into the container, and install ollama and run it.
3. Download Llama 4 (took forever to download), and run it.

At this point we can already ask it questions through the terminal.

In the next tutorial I am going to walk you through the steps on how to locally spin up a docker container running OpenWebUI.This week, I decided to dive into something I’ve been curious about for a while: setting up my own private LLM to help with coding. With all the buzz around next-gen AI coding assistants—some even claiming they'll replace developers—it felt like the right time to see what’s really possible.

At first, I assumed I could just run one of these models locally on my own machine. How hard could it be, right? That illusion didn’t last long. After digging into some documentation, I realized LLaMA 4 demands an outrageous amount of computing power—specifically, something like 96GB of VRAM and ideally four GPUs. Yeah, my laptop wasn’t going to cut it.

Rather than giving up, I turned to RunPod, a service that lets you rent powerful GPU infrastructure on demand. It seemed like the perfect workaround: I could run the actual model in the cloud and connect to it from my local environment whenever I needed.

Once I created an account on RunPod, I spun up a pod with the right specs. The key was selecting a machine with at least 96GB of VRAM and ensuring that port 11434 was exposed, since that’s how I’d be connecting to the model later. Once the pod was running, I connected to it via SSH and got to work.

Inside the container, I installed Ollama—a neat little tool that makes working with LLMs surprisingly straightforward.

curl -fsSL https://ollama.com/install.sh | sh

With Ollama in place, I started downloading LLaMA 4, which honestly took forever. But eventually, the model was ready to run.

When I finally fired it up, I was able to chat with it directly from the terminal. No fancy UI, just raw command-line interaction with a seriously capable language model. And honestly? It was kind of amazing. I could ask it questions, get code suggestions, and brainstorm ideas—all without needing a public API key or sending my code snippets to some external service.

Of course, using the terminal isn’t exactly the most pleasant experience long-term. So in the next post, I’ll show you how I set up OpenWebUI in a local Docker container to give this model a proper user interface. It’s way more fun to use and makes the whole thing feel like a polished, personal coding assistant.

For now, though, I’m already impressed by what’s possible with just a few tools and a little rented compute power. The AI hype may be real—but it turns out, it’s also something you can play with yourself.