Stop Paying $20/Month for GitHub Copilot. Build Your Own in 10 Minutes.

Key Takeaways

• You can replace GitHub Copilot with a completely local setup using free, open-source tools
• Ollama handles the heavy lifting of running AI models locally with GPU acceleration
• The Continue VS Code extension seamlessly replaces Copilot's interface
• DeepSeek-Coder models are optimized for coding and run on standard laptops with 16-32GB RAM
• Your code never leaves your machine, eliminating privacy concerns entirely

Let's talk about the elephant in the room. You're probably shelling out $20 every month for GitHub Copilot. Maybe you've justified it because hey, it saves time. But here's what's actually happening: your code is getting shipped off to Microsoft's servers, processed in their cloud, and who knows what happens to it after that. Training data, anyone?

And that's not even mentioning the lag. The second your WiFi hiccups, Copilot becomes about as useful as a chocolate teapot. Server outage on Microsoft's end? Good luck getting any work done.

So here's the thing. The hardware landscape has shifted dramatically. Your laptop, the one sitting in front of you right now, can probably run state-of-the-art AI models locally. No internet required. No monthly fees. No corporate servers touching your proprietary code. This isn't some futuristic fantasy. This is something you can set up before your coffee gets cold.

Understanding the Three-Layer Stack

Before we dive into the actual setup, you need to understand what we're building here. A local AI coding assistant has three distinct layers, and each one does something specific.

• The Inference Engine (Ollama): Think of this as the muscle. It loads AI models into your computer's RAM or VRAM and serves them locally through an API. All the complex GPU acceleration stuff? Ollama handles it silently.
• The Brain (DeepSeek): This is the actual language model that understands code. It's been trained on massive amounts of programming data and knows how to autocomplete, explain, and generate code.
• The Interface (Continue.dev): This VS Code extension replaces Copilot's sidebar and autocomplete functionality. Instead of sending requests to the cloud, it talks to your local Ollama server.

Pretty elegant when you think about it. Three open-source components working together to give you something that rivals a $20/month subscription.

Step 1: Install Ollama (Your Local Engine)

Ollama has become the standard for running LLMs locally, and for good reason. It abstracts away all the painful stuff like CUDA configurations and memory management. You just install it and it works.

If you're on Linux or using WSL, pop open your terminal and run this:

Once that finishes, verify everything's working:

You should see a version number spit back at you. That means your local server is ready to accept models. Easy, right?

Step 2: Pull the Right Model (Reality Check Time)

Okay, I need to be honest with you here because a lot of tutorials aren't. The full DeepSeek-V3 model? You're not running that on your laptop. It's a massive Mixture-of-Experts model that needs serious server-grade hardware. Anyone telling you otherwise is lying for clicks.

But here's the good news. You don't need the full model anyway. For coding tasks, the distilled and quantized variants work incredibly well. If you've got 16-32GB of RAM, the DeepSeek-Coder series is what you want.

This pulls a 6.7 billion parameter model that's specifically optimized for code. It'll handle autocomplete, code explanation, and generation without breaking a sweat on most modern machines.

Step 3: Install the Continue Extension

Now for the interface layer. Continue is a VS Code extension that's been gaining serious traction as an open-source Copilot alternative. The beautiful part? It's designed to work with local models out of the box.

1. Open VS Code and hit Ctrl+Shift+X (or Cmd+Shift+X on Mac) to open the Extensions panel
2. Search for 'Continue' and install the official extension from Continue.dev
3. Once installed, you'll see a new Continue icon in your sidebar
4. Click it to open the Continue panel and start configuration

The extension will walk you through initial setup, but the key thing is pointing it at your local Ollama instance. By default, Ollama runs on localhost:11434, and Continue knows to look there.

Step 4: Connect Everything Together

With Ollama running and Continue installed, you need to configure Continue to use your local DeepSeek model. Open the Continue configuration (there's a gear icon in the Continue panel) and you'll see options for choosing your model provider.

Save that config and you're basically done. Start typing in any code file and you'll see autocomplete suggestions powered by your local model. No internet required. No data leaving your machine.

What You're Actually Getting

Let me break down why this setup is actually kind of incredible.

The tradeoffs are real, but for most developers they're worth it. Especially if you're working with sensitive or proprietary code that absolutely shouldn't be hitting external APIs.

Performance Tips That Actually Matter

A few things I've learned from running this setup daily:

• Keep Ollama running in the background. Starting it fresh every time adds unnecessary delay.
• If you're on a laptop, plug in when doing heavy coding sessions. Local inference isn't exactly gentle on your battery.
• Try different quantization levels. The :6.7b model is a sweet spot, but :1.3b runs faster on weaker hardware.
• Clear Ollama's cache occasionally if you've been pulling multiple models. They eat disk space fast.

The Bigger Picture Here

Look, this isn't just about saving $20 a month. Though that's nice too. This is about a fundamental shift in how developers can work with AI tools.

For years, the assumption was that AI required massive cloud infrastructure. You had to rent access from big tech companies. Your data had to flow through their pipes. That's changing. Fast.

The models are getting smaller and more efficient. The hardware is getting more capable. Tools like Ollama are making local deployment dead simple. We're entering an era where owning your AI tools isn't just possible, it's practical.

And honestly? Once you experience zero-latency code completion that works on an airplane, you're not going back to cloud-dependent tools. The difference is that noticeable.

What's Next?

This basic setup will cover most of your Copilot use cases. But you can go deeper. Continue supports multiple models, so you could run a smaller fast model for autocomplete and a larger one for complex code generation. You can also add local embedding models for better codebase understanding.

The rabbit hole goes as deep as you want it to. But for now? You've got a working private Copilot that didn't cost you anything. That's a pretty solid win for 10 minutes of work.

Frequently Asked Questions

Will this work on Apple Silicon Macs?

Yes, and actually quite well. Ollama has excellent Metal support, so M1/M2/M3 Macs can run these models efficiently using their unified memory.

Can I use this with other editors besides VS Code?

Continue currently focuses on VS Code and JetBrains IDEs. For other editors, you might need different interface tools, but Ollama itself works with anything that can hit a REST API.

How does the quality compare to GitHub Copilot?

Honestly? For most day-to-day coding, it's comparable. Copilot sometimes has an edge on very new libraries or niche frameworks, but DeepSeek-Coder handles standard development tasks really well.

What if I only have 8GB of RAM?

You'll need to use smaller models like deepseek-coder:1.3b. Performance won't be as good, but it'll still work. Consider it a stepping stone until you upgrade your hardware.

Sources & Credits

Originally reported by DEV Community