Stop Paying for GitHub Copilot: Build a Free, 100% Private AI Assistant Locally

The landscape of AI coding assistants has completely shifted. While cloud-based subscriptions like GitHub Copilot or Claude Pro are excellent, they come with two major pain points: recurring monthly costs and privacy concerns regarding your proprietary code.

Thanks to the incredible advancements in efficiency, you can now run state-of-the-art coding LLMs directly on your local machine with zero latency, absolute privacy, and absolutely no cost.

In this guide, we will set up a blazing-fast, context-aware local AI coding assistant using Ollama and Continue.dev in VS Code.

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Why Go Local?

• 💰 Cost-Efficient: $0/month forever.
• 🔒 100% Private: Your code never leaves your local machine. Perfect for NDA-protected or enterprise projects.
• ✈️ Offline Capability: Code with full AI assistance on a plane, a train, or anywhere without internet.
• 🚀 Customization: Swap models instantly depending on whether you need quick autocomplete or deep architectural reasoning.

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Prerequisites

To get a smooth experience, you ideally need:

• A modern machine (Apple Silicon M-series chips or a Windows/Linux PC with a dedicated Nvidia RTX GPU).
• 16GB of RAM/VRAM minimum (8GB can work with highly compressed models, but 16GB+ is the sweet spot).

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Step 1: Install Ollama and the Coding Model

Ollama is the easiest way to manage and run LLMs locally.

1. Download and install Ollama for your OS.
2. Open your terminal and run the following command to download Qwen2.5-Coder (7B) or DeepSeek-Coder. For most modern setups, the 7B (7-billion parameter) model offers the ultimate balance between speed and ChatGPT-4 level coding intelligence.

ollama run qwen2.5-coder:7b

Note: If your machine has lower specs, try ollama run qwen2.5-coder:1.5b for lightning-fast autocompletion.

Once the download is complete, you can test it in the terminal, then minimize it. Ollama will run quietly in the background as a local API.

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Step 2: Set Up Continue.dev in Your IDE

Continue is an open-source AI code assistant extension that seamlessly replaces the Copilot UI in VS Code or JetBrains IDEs.

1. Open VS Code.
2. Go to the Extensions marketplace (Ctrl+Shift+X or Cmd+Shift+X).
3. Search for Continue and click Install.

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Step 3: Configure Continue to Use Your Local Model

Once installed, a new icon will appear in your sidebar. Click it, then click the gear icon (⚙️) at the bottom right of the Continue panel to open your config.json file.

Replace the contents of the file with the following configuration to link it to your local Ollama instance:

{
  "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 comprehensive unit test suite for the code above using Jest/Vitest.",
      "description": "Write unit tests"
    }
  ],
  "contextProviders": [
    { "name": "codebase", "params": {} },
    { "name": "openFiles", "params": {} }
  ]
}

What this configuration does:

• Chat Model: Uses the robust 7b model for complex tasks, refactoring, and general chat conversations in the sidebar (Cmd+L or Ctrl+L).
• Tab Autocomplete: Uses the ultra-lightweight 1.5b model to instantly suggest inline code as you type, ensuring zero lag.
• Context Awareness: Allows you to type @codebase in the chat to let the local AI read your entire project repository securely.

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How to Maximize Your Local AI Workflow

Now that you are fully set up, here are three essential shortcuts to replace your paid workflow:

1. The Inline Edit (Cmd+I / Ctrl+I)

Highlight a block of code, press Cmd+I, and ask the local model to modify it directly. For example: "Refactor this fetch request to use async/await and add error handling."

2. Full Project Context (@codebase)

If you are debugging a tricky bug that spans multiple files, open the chat and type:

@codebase why is my authentication state resetting on page refresh?

The extension will index your local files locally, feed relevant snippets to Ollama, and give you an exact answer without a single byte uploaded to the cloud.

3. Automatic Doc Generation

Highlight a function, hit your chat shortcut, and ask: "Add JSDoc comments to this function explaining the parameters."

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Final Thoughts

The era of paying $10-$20 a month per developer for basic AI autocompletion is coming to an end. By leveraging open-weights models and local orchestration tools, you gain total control over your development environment, secure your data, and save money.

Give it a spin and see how it handles your toughest codebases!

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Connect with Me

If you found this guide helpful, let's connect and discuss modern development workflows!

• 💻 GitHub: johnnylemonny
• ✍️ DEV.to: johnnylemonny

Comments

[Hossein Yazdi]

I like this. The combination of Ollama and Continue has made local AI much more practical than it was a year ago. For developers working with private codebases, the privacy aspect alone can be a huge advantage.

Also, DeepSeek and other coding-focused AI tools have improved a lot recently, with more options beyond the usual paid subscriptions.

[𝗝𝗼𝗵𝗻]

Thanks, Hossein, really appreciate that perspective. I agree, the pace of improvement in local, coding‑focused models has been impressive, and having solid alternatives to paid subscriptions is a big win, especially when privacy matters.

[Alex Shev]

Local coding assistants are getting practical, but the setup should be judged by more than privacy and price. Context quality, latency under real projects, and how the tool handles repo-specific instructions are what decide whether it sticks.

[𝗝𝗼𝗵𝗻]

Thanks for reading and commenting, Alex - really appreciate the feedback.

[Alex Shev]

Appreciate it. Local assistants are getting good enough that the real question is shifting from "can it run locally?" to "can I trust the workflow around it?" That is where setup, privacy, model choice, and evaluation start to matter more than the demo.

[𝗝𝗼𝗵𝗻]

Spot on! We've definitely crossed the threshold of "is this even possible?" and entered the era of "is this actually practical for my daily grind?" Trusting the workflow is exactly why the open-source ecosystem around tools like Continue and Ollama is so crucial. When you have full control over the model choice, transparent setup, and an absolute guarantee of privacy, it stops being a novelty demo and becomes a reliable, foundational part of your toolchain. Couldn't have said it better myself!

[Alex Shev]

Exactly. Local-first becomes compelling when it changes the trust model, not just the bill. The next practical layer is evaluation: repo-specific checks, latency under real projects, and whether the assistant respects local instructions when the codebase gets messy. That is where the demo turns into a toolchain.

[Nazar Boyko]

A 7B model matching GPT-4 on coding is a bold line. Generally these small coder models feel great for filling in a function and noticeably thinner the moment a task needs to hold a lot of context at once, which is exactly where the cloud models still pull ahead. The privacy and offline angle is the real selling point here, and honestly the post doesn't need the GPT-4 comparison to land. Have you leaned on the 7B for anything heavier than autocomplete, like a refactor across a few files, and did it hold up?

[𝗝𝗼𝗵𝗻]

That is an incredibly fair point, and I appreciate you calling it out! You are absolutely right that the "matches GPT-4" claim comes with an asterisk. It holds up well on specific, isolated coding benchmarks (like HumanEval), but you hit the nail on the head regarding the context window. To answer your question about heavier tasks: I have used the 7B model for cross-file refactoring, but it requires much more "babysitting" of the context compared to cloud giants. If I use @openFiles with 2 or 3 small, strictly related components (for example, migrating a specific React Context to Zustand), it holds up surprisingly well and gets the job done. However, if I try to dump an entire directory into the context or ask for a sweeping, repository-wide architectural change, it definitely starts dropping threads or hallucinating - which is exactly where the flagship cloud models still wear the crown. ​I completely agree with your takeaway. The true value here isn't that it replaces cloud models for massive architectural overhauls, but rather the absolute privacy, zero-latency, and offline capabilities. It handles the 80% of daily focused logic and boilerplate flawlessly for free, letting you save the API costs for the 20% of tasks that actually require that massive cloud compute. Thanks for reading and for the highly insightful comment!