Have you ever wanted an AI coding assistant inside VS Code without paying for GitHub Copilot or other monthly subscriptions?

The good news is—you can.

Using Qwen 3 and Ollama, you can run a powerful coding model directly on your computer and use it inside VS Code.

No API costs.

No cloud dependency.

Your code stays on your machine.

📺 Watch the Complete Video Tutorial

🎥 YouTube Video:


How to Use Qwen 3 with VS Code (FREE) | Complete Setup with Ollama

What You'll Learn

By the end of this tutorial you'll know how to:

• Install Ollama


• Download Qwen 3


• Connect Qwen 3 with VS Code


• Configure OpenCode


• Generate your first AI code


• Fix common setup issues


• Use local AI effectively

What is Qwen 3?

Qwen 3 is Alibaba's latest family of open-source large language models.

It performs well for:

• Code Generation


• Debugging


• Code Explanation


• Refactoring


• General Programming Tasks

Since it can run locally using Ollama, you don't need to rely on cloud APIs for everyday coding assistance.

What is Ollama?

Ollama lets you run Large Language Models directly on your computer.

Benefits include:

• Privacy


• No API Charges


• Offline Support


• Fast Local Responses


• Easy Model Management

Step 1 — Install Ollama

Download Ollama from:

https://ollama.com

Verify installation:

ollama --version

Step 2 — Download Qwen 3

Run:

ollama pull qwen3.6:latest

Verify:

ollama list

You should now see the Qwen 3 model installed locally.

Step 4 — Connect Qwen 3 with VS Code

Configure OpenCode to use your Ollama server.

Typical endpoint:

http://localhost:11434

or

http://localhost:11434/v1

depending on your configuration.

Step 5 — Generate Your First Code

Instead of asking:

Build an entire React application

Start with something simple.

Example:

Create a reusable React Button component using functional components and CSS Modules.

Small prompts generally produce better results with local models.

Common Mistakes

❌ Asking for an entire application

Instead:

Break the project into smaller components.

❌ Choosing the wrong model

Use a coding-capable Qwen model whenever possible.

❌ Expecting cloud-model behavior

Local models work best when you:

• Keep prompts focused


• Iterate gradually


• Review generated code

My Experience

After experimenting with Qwen 3, I realized that the biggest limitation wasn't the model itself—it was my prompting strategy.

Once I started building projects step by step instead of expecting an entire application in one response, the quality improved significantly.

What's Next?

This article is Part 1 of the series.

In Part 2 we'll build a complete GitHub Profile Finder using:

• React


• GitHub API


• Qwen 3


• VS Code


• Ollama

…all with step-by-step prompts.

Resources

Ollama

https://ollama.com

Qwen Models

https://ollama.com/library/qwen3

VS Code

https://code.visualstudio.com/

Conclusion

Running Qwen 3 locally inside VS Code is a great option if you want a private, free AI coding assistant.

Whether you're learning React, JavaScript, or working on professional software projects, local AI models have become capable enough to be part of your daily development workflow.

🎥 Prefer Watching?

If you found this article helpful, consider following TheCodeStreet for more tutorials on:

• AI


• .NET


• C#


• Ollama


• Qwen


• AI Agents


• Semantic Kernel


• VS Code


• Software Development

Happy Coding! 🚀