The rise of AI coding assistants like GitHub Copilot, ChatGPT, and Claude has undeniably supercharged developer productivity. But that convenience comes at a cost: data security risk.
If you work on proprietary company projects, handle sensitive client data, or are building an unpatented startup product, shipping every line of code to a third-party cloud server is a serious violation of organizational security policy.
Here's the good news: you can now build a 100% offline AI coding assistant that runs entirely on your own machine. By combining the power of Ollama with the popular Continue extension in VS Code, you get a blazing-fast, intelligent AI Copilot — without a single line of code ever leaving your computer.
---
### Why a Local AI Copilot?
Beyond security, going local-first with your AI coding assistant unlocks several other advantages:
1. Maximum Privacy (100%): All your code is processed exclusively in your own RAM and GPU. No data is ever sent to external servers for model training or logging.
2. Fully Offline Operation: Whether you're coding on a flight, in a café with spotty Wi-Fi, or in a remote location, your AI assistant keeps working at full speed — no internet required.
3. No Monthly Subscription: Say goodbye to GitHub Copilot's recurring fees. The models you use are open-source and free to run indefinitely.
4. Model Flexibility: Swap models on the fly. Use a larger model for deep code analysis and debugging, or a smaller, faster model for real-time autocomplete — your choice, every time.
---
### Prerequisites
Before you dive in, make sure your hardware can handle the workload smoothly:
* CPU & RAM: A minimum of 16 GB RAM is recommended. Apple Silicon (M1/M2/M3) or a dedicated Nvidia GPU will give you a significant speed boost.
* Code Editor: VS Code (Visual Studio Code) or Cursor installed on your machine.
* Ollama: The Ollama application installed and running on your system.
---
### Step 1: Download a Coding Model via Ollama
Large language models fine-tuned specifically for code (Code LLMs) have reached remarkable quality. In this guide, we'll use the Qwen2.5-Coder family by Alibaba — widely regarded as one of the best value-for-hardware coding models available today.
Open your Terminal (or Command Prompt) and run the following commands to pull the models:
1. Primary Chat & Edit Model:
The 7B variant excels at answering questions, analyzing errors, and writing new code.
```bash
ollama run qwen2.5-coder:7b
```
2. Tab Autocomplete Model:
The 1.5B variant is ultra-lightweight, delivering instant suggestions every time you hit Tab.
```bash
ollama run qwen2.5-coder:1.5b
```
Once the downloads finish, both models are ready and waiting on your machine.
---
### Step 2: Install the "Continue" Extension in VS Code
Continue is an open-source AI assistant framework designed to connect your code editor to any LLM — including local models served through Ollama.
1. Open VS Code and navigate to the Extensions panel on the left sidebar (or press Ctrl+Shift+X on Windows / Cmd+Shift+X on macOS).
2. Search for "Continue".
3. Click Install to add the extension to your VS Code.
---
### Step 3: Configure Continue to Connect to Ollama
After installation, the Continue icon will appear in your left sidebar. Follow these steps to wire it up to the models you downloaded:
1. Click the Continue icon, then click the gear (Settings) icon in the bottom-left corner of the Continue panel to open the config.json file.
2. Delete all existing content and replace it with the following configuration to set Ollama as your primary inference engine:
{
"models": [
{
"title": "Qwen2.5 Coder 7B",
"provider": "ollama",
"model": "qwen2.5-coder:7b"
}
],
"tabAutocompleteModel": {
"title": "Qwen2.5 Coder 1.5B",
"provider": "ollama",
"model": "qwen2.5-coder:1.5b"
},
"customCommands": [
{
"name": "test",
"prompt": "{{{ input }}}\n\nWrite a complete test suite for this code.",
"description": "Write a test suite"
}
],
"contextProviders": [
{
"name": "code",
"params": {}
},
{
"name": "docs",
"params": {}
}
],
"slashCommands": [
{
"name": "edit",
"description": "Edit selected code"
},
{
"name": "comment",
"description": "Write comments for selected code"
},
{
"name": "share",
"description": "Share selected code"
}
]
}
3. Save the file. Continue will automatically connect to your local Ollama instance.
---
### Putting It to Work
Once everything is set up, you can immediately use the core features of your Local AI Copilot:
* Chat with AI: Press Ctrl+L (or Cmd+L on Mac) to open the side chat panel. Select any block of code in your project and hit the shortcut to ask questions directly — hunt down bugs, request explanations, or brainstorm refactors.
* Tab Autocomplete: As you type, the 1.5B model works in the background and displays ghost-text suggestions. If you like what you see, just press Tab to accept the completion instantly.
Inline Edit: Select the code you want to modify, then press Ctrl+I (or Cmd+I on Mac). Type your instruction in plain language — e.g., "Refactor this function to use async/await"* — and the AI will rewrite the code in place, replacing the original.
---
### The Bottom Line
Building a 100% offline AI coding assistant with Ollama and Continue does more than protect your intellectual property and sensitive source code from leaking to external clouds — it puts you back in the driver's seat. No internet dependency. No monthly billing. No third-party data pipelines.
If you want to boost your development speed while keeping your data sovereignty intact, follow this guide and start using your own Local AI Copilot today.