🧠 Exploring Offline LLMs

#llm #ollama #vscode #githubcopilot

Recently, I’ve been diving deep into how we can run Large Language Models (LLMs) completely offline on our own machines. One of the first things I discovered was Ollama, which is an incredible tool that allows you to run LLMs locally—yes, completely offline compatible!

🚀 Initial Setup with Ollama + WebUI

During my research, I came across an interesting interface called WebUI, which runs a beautiful frontend on top of Ollama. This combo lets you:

Pull models (like Mistral, LLaMA, etc.)
Host them via a local server
Interact with them through a chat-like UI

But here’s the catch: WebUI requires Docker, and running Docker along with these models demands high hardware resources. Unfortunately, my system couldn’t handle that load. The UI was either unresponsive or the model would crash midway. So, I decided to uninstall WebUI and keep things simple.

🧩 Lightweight LLMs with Ollama Only

Next, I used Ollama standalone with selected models. This worked perfectly fine for text generation without a graphical interface. I could run commands in the terminal, and the responses were quick—even on my mid-range setup. This confirmed that Ollama is truly a minimal and efficient way to run LLMs locally.

💡 The Unexpected Discovery: GitHub Copilot with Local Models

Here’s where things got really interesting. While experimenting with coding tools, I found out that GitHub Copilot in VS Code (with certain plugins and configurations) can now run local LLMs without Docker and with very low hardware usage! 🤯