Run Gemma 4 Locally with GitHub Copilot and VS Code

Local AI development is getting really interesting.

For a long time, using AI coding assistants meant relying almost entirely on cloud-hosted models. You installed an extension, signed in, and your prompts, code context, and completions were handled by a remote model provider. That model was usually powerful, fast, and convenient, but it also came with limits, pricing tiers, privacy considerations, and a dependency on an internet connection.

Now things are changing.

With tools like Ollama, models like Gemma 4, and editor integrations in Visual Studio Code, it is becoming much more practical to run capable AI models locally on your own machine. Combine that with GitHub Copilot Free, and you can start experimenting with local AI-powered development workflows without needing to burn through cloud-hosted token usage for every prompt.

This is not the same as saying a small local model completely replaces GPT or Opus-class cloud models in every situation. That would be overstating it. But for many day-to-day development tasks, local models are becoming surprisingly useful.

And that is a big deal.

The Basic Idea

The setup looks something like this:

You use VS Code as your editor.

You use GitHub Copilot for the chat and coding assistant experience.

You install Ollama locally to run models on your own machine.

Then you run a model like Gemma 4 locally and connect it into your development workflow.

The result is a coding assistant experience where at least some of the AI interaction can happen locally instead of being sent to a remote cloud model.

That matters for a few reasons.

First, local models are not metered the same way cloud models are. Once the model is downloaded and running on your machine, you are mostly limited by your hardware. You are not paying per token to the local model provider. You are not waiting for an API quota to reset. You are not worrying about whether a small experiment is worth spending credits on.

Second, local models can be useful for privacy-sensitive work. If you are working on internal scripts, infrastructure files, configuration, or exploratory code, keeping the interaction local can be attractive. It does not magically solve every security concern, but it does give you more control over where your prompts and code context are processed.

Third, this lowers the barrier to experimenting with AI development workflows. Developers can try things, break things, and iterate without feeling like every prompt is consuming a paid cloud request.

GitHub Copilot Free and Local Models

GitHub Copilot Free gives developers a way to get started with Copilot without immediately committing to a paid plan. However, it is important to be precise about what is free and what is unlimited.

Copilot Free itself may still have plan limits for GitHub-hosted Copilot features. That means you should not assume every Copilot-hosted chat request or completion is unlimited just because you are using the free tier.

The local model part is different.

When you run a model locally through something like Ollama, the actual inference happens on your machine. That means the local model usage is constrained by your hardware, not by a hosted model’s token quota. In practice, this lets you do a lot of experimentation locally without worrying about cloud usage limits.

That is the exciting part of this workflow.

You can use Copilot and VS Code as the familiar interface, but bring in a local model for certain tasks. This gives you a hybrid approach: use local AI where it makes sense, and still reach for larger cloud models when you need their extra reasoning power, broader context handling, or better performance.

Why Ollama Matters

Ollama has become one of the easiest ways to run language models locally.

Instead of manually downloading model weights, configuring runtimes, dealing with low-level dependencies, and figuring out model serving yourself, Ollama simplifies the process. You install Ollama, pull a model, and run it locally.

For developers, that is exactly the kind of workflow that makes experimentation practical.

You do not want to spend an entire afternoon getting a model server running just to test whether a local AI assistant can help write a function, explain an error, or generate a unit test. You want something that feels as approachable as installing a developer tool.

That is where Ollama fits nicely.

For example, the workflow is generally as simple as:

ollama run gemma4

The exact model name and availability may vary depending on what Ollama supports at the time you are setting this up, but the concept is straightforward: download the model locally, run it, and connect your tools to it.

Once that is working, you can start experimenting with local prompts directly from the terminal or through integrations in VS Code.

How Good Is Gemma 4?

This is where it is important to be honest.

Gemma 4, like other modern open-weight models, can be very impressive. For a locally runnable model, it may perform well on many programming-related tasks, especially when the task is focused and the context is clear.

It can help explain code.

It can summarize a file.

It can draft small functions.

It can help write documentation.

It can generate examples.

It can review snippets.

It can help brainstorm architecture options.

It can assist with command-line usage, configuration, and common development questions.

That is already incredibly useful.

However, saying that Gemma 4 is “basically the same as GPT or Opus” is too broad. Model comparisons depend on the task, the exact model size, quantization, prompt quality, available context, tool use, and the hardware you are running on.

A small local model may feel excellent when working on a short, well-scoped coding question. The same model may struggle with a large refactor across many files, complex reasoning, long context, or nuanced architectural tradeoffs.

That does not make it bad.

It just means local models should be viewed as another tool in the toolbox.

For many tasks, a local model is good enough and dramatically more convenient. For harder tasks, a larger hosted model may still be the better choice.

The Hardware Question

Running AI locally depends heavily on hardware.

A modern MacBook Pro, especially one with Apple Silicon and enough unified memory, can be a very capable local AI development machine. Models that would have been painful to run on a laptop a few years ago are now much more practical.

That said, performance will vary.

A smaller model may respond quickly.

A larger model may be noticeably slower.

Quantized models may run with lower memory requirements but can trade off some quality.

Your available RAM, GPU acceleration, thermal limits, and model size all matter.

So yes, you can absolutely run capable models locally today. But the experience will not be identical for everyone. A newer high-end laptop will feel very different from an older machine with limited memory.

This is one reason I like the hybrid approach. Run local models when they are fast enough and good enough. Use cloud-hosted frontier models when the task benefits from more horsepower.

Why This Feels Empowering

The most exciting part of this setup is not just cost savings.

It is the feeling of control.

As developers, we like tools that we can install, configure, script, automate, and understand. Local AI brings some of that feeling back to AI-assisted development.

Instead of every interaction depending on a hosted service, you can run a model on your own machine. You can test different models. You can compare results. You can use one model for code explanation, another for documentation, another for quick terminal help.

This starts to feel less like “AI as a remote product” and more like “AI as part of the local developer workstation.”

That shift is important.

It opens the door for more personalized workflows. It also makes AI more accessible to developers who want to learn how these systems behave without constantly thinking about pricing or quota limits.

Where Local AI Works Well

In my experience, local AI is especially useful for focused tasks.

For example, asking a local model to explain a short piece of code can work very well. Asking it to generate a simple helper function is often useful. Asking it to draft README content, summarize a configuration file, or suggest test cases can be a great fit.

Local models are also good for repetitive development chores.

Things like:

Explain this error message.
Write a basic unit test for this function.
Convert this JavaScript snippet to TypeScript.
Generate sample JSON for this schema.
Summarize what this script does.
Create documentation comments for this method.
Suggest edge cases I should test.

These are tasks where you do not always need the largest model available. You need something fast, convenient, and good enough.

That is exactly where local models can shine.

Where Cloud Models Still Win

There are still plenty of situations where I would reach for a larger cloud model.

Complex debugging across multiple files is one of them. Large-scale refactoring is another. Deep architectural reviews, advanced reasoning, security-sensitive analysis, and long-context code understanding can still benefit from more powerful hosted models.

Cloud models are also usually better integrated with advanced agent workflows, retrieval systems, tool calling, and large context windows. That may change over time, but today it is still a meaningful difference.

The key is not to turn this into a local-versus-cloud argument.

The better view is local-plus-cloud.

Use the right model for the right job.

A New Era for Developer Workstations

What excites me most is where this is heading.

We are moving toward a world where a developer workstation can include a local AI assistant as a normal part of the environment. Just like we install Git, Docker, Node.js, Python, .NET, or PowerShell, we may increasingly install local models as part of our standard setup.

That changes expectations.

Developers will expect local AI to be available.

Editors will expect to connect to multiple model providers.

Teams will think more carefully about when to use hosted models and when to keep work local.

Hardware will continue to improve.

Models will continue to get smaller, faster, and more capable.

This is the beginning of something much bigger than a single model or a single editor extension.

Final Thoughts

Using Gemma 4 locally with Ollama, GitHub Copilot, and VS Code is not magic, and it is not a full replacement for every cloud-hosted AI model. But it is absolutely worth paying attention to.

The ability to run a capable AI model locally, interact with it from your editor, and use it as part of your everyday coding workflow is extremely powerful.

It gives developers more flexibility.

It reduces dependency on hosted token usage for many common tasks.

It can improve privacy for certain workflows.

And perhaps most importantly, it makes AI experimentation feel accessible.

We are entering a phase where AI is no longer only something you call through a remote API. Increasingly, it is something you can run on your own machine, wire into your own tools, and adapt to the way you work.

The increased productivity and quality outputs from local AI models brings a major shift in the industry.

And, the transformation is going to be really interesting!

Original Article Source: Run Gemma 4 Locally with GitHub Copilot and VS Code written by Chris Pietschmann (If you're reading this somewhere other than Build5Nines.com, it was republished without permission.)