Every developer writing code today has a copilot open somewhere. It sits in the IDE, it autocompletes, it chats, it explains. It has become as natural as syntax highlighting. And for most teams, the copilot is quietly sending every prompt, every function name, every variable, every piece of business logic, to a cloud server somewhere.

Most people have accepted that as the deal. You get smart suggestions; they get your code.

Today, that deal changes.

OneInfer Edge now lets you run your existing copilot on a locally deployed AI model, with no plugin to install, no IDE settings to change, and a single button to switch.

The copilot stays exactly as you know it. The model moves to your machine. Your prompts never leave.

Why this matters

The productivity argument for coding copilots is settled. Developers who use them ship faster. That is not the debate. The debate is what happens to your code while it does.

Enterprise teams working on proprietary systems. Developers at fintech or healthcare companies with data residency requirements. Solo builders who just do not want their architecture ideas living on someone else’s server. The list of people who want AI assistance without the data exposure is long, and until now, their only option was to turn the copilot off.

OneInfer Edge offers a third option: keep the copilot, move the model.

What is the AI Coding Tool feature

Built directly into the OneInfer Edge desktop app, the AI Coding Tool panel connects your locally deployed model to the coding tools you already use. No middleware to set up. No custom extensions. No configuration files to edit in your IDE.

OneInfer Edge runs a local proxy in the background. When you click ONEINFER for any supported copilot, that proxy intercepts the copilot’s requests, translates them into the correct format for your local model, routes them to the running inference endpoint on your machine, and returns the response, all invisibly, all locally.

From your IDE’s perspective, nothing changed. From your privacy and cost perspective, everything did.

Supported copilots at launch:

• OpenCode -Install OpenCode if needed and write a OneInfer-backed user config
• Kilo Code -Install Kilo Code if needed and write a OneInfer-backed user config
• OpenClaw -Install OpenClaw if needed and write a OneInfer-backed user config
• Codex -Install Codex if needed and write a OneInfer-backed user config

Each copilot appears in the AI Coding Tool panel with two buttons: ONEINFER to route through your local model, and the copilot’s own button to go back to cloud. Switch at the start of a session or mid-session, whenever makes sense for the work you are doing.

How it works, the full picture

The feature builds on OneInfer Edge’s local deployment infrastructure. If you have already deployed a model locally, you are one click away from using it as your copilot backend.

Step 1: Deploy a local model In the Self Hosting section, paste any Hugging Face model ID. OneInfer Edge scans your machine, GPU, VRAM, OS, installed serving libraries, and gives you a Hardware Ready verdict before you run anything. It accounts for model weights, KV cache, and serving library overhead, not just raw file size.

Step 2: The model goes online One click deploys via Ollama or llama.cpp and registers a local inference endpoint at http://127.0.0.1:11434/v1. The model status shows as Online in your dashboard.

Step 3: Switch your copilot On the AI Coding Tool panel, click ONEINFER next to your copilot. OneInfer Edge starts the proxy and begins routing that copilot’s requests through your local model, handling all format translation automatically.

Step 4: Code normally Open your IDE. Use the copilot exactly as before. Completions, chat, explanations, they all work. The only difference is where the inference happens: on your machine, not in the cloud.

Step 5: Switch back anytime Need the cloud model for a complex reasoning task? Click the copilot’s own button and you are back on cloud instantly, without restarting anything.

What the proxy does under the hood:

[Proxy] Mapping Codex responses format to OpenAI messages format...
[Proxy] Model 'gpt-5.4-mini' not found in local server models list
[Proxy] Rewriting model to hf.co/Qwen/Qwen2.5-0.5B-Instruct-GGUF:latest
[Proxy] Intercepted streaming request. Keeping stream: true
[Proxy] Forwarded server responded: 200
[Proxy] Responses API stream translation complete.

The proxy intercepts the request, rewrites the model reference to your local deployment, translates the response format, and returns it to the copilot. From Codex’s perspective, it received a normal response. From your machine’s perspective, it never left.

What makes this different from other approaches

There are tools that let you self-host a model and configure a custom endpoint manually. You can point Ollama at a base URL, edit a JSON config, restart the IDE, and sometimes it works. Sometimes the model name does not match what the copilot expects. Sometimes the response format is wrong and suggestions stop working. Debugging it takes an afternoon.

OneInfer Edge handles all of that. The format translation, the model name rewriting, the endpoint registration, it is all abstracted away. The feature works because OneInfer sits between the copilot and the model and speaks both languages.

1. Zero config, No plugins, no IDE changes, no config files. Just click ONEINFER and the local proxy handles everything
2. True privacy, Your prompts never leave your machine. Inference runs locally, the only cost is electricity and hardware amortization
3. Instant switching, Go from local to cloud and back in one click, at any point in a session, no restart required
4. Your hardware, your speed, Latency depends on your machine, not a network. No GPU cold starts, no rate limits, no queue

On latency: Because the model runs entirely on your local machine, response speed is a function of your hardware configuration, not network conditions or server load. A capable machine with sufficient VRAM will produce fast, consistent completions with no queue, no rate limits, and no cold starts.

The bigger picture

OneInfer Edge was built on a single belief: self-hosting AI should be a genuine alternative to managed cloud inference, not a punishment for developers who do not want to pay per token.

The AI Coding Tool feature extends that belief to the tools developers use every day. You should not have to choose between a good copilot experience and keeping your code private. You should not have to maintain a fragile custom endpoint setup just to run local LLM inference. And you should not be locked into a single provider when the model you want is sitting on your hard drive.

This feature makes local AI for developers practical for the workflow that matters most: writing code.

Frequently asked questions

Which copilots are supported at launch? OpenCode, Kilo Code, OpenClaw, and Codex. More will follow based on demand.

Do I need to install a plugin in my IDE? No. OneInfer Edge requires no plugin and no changes to your IDE setup. The proxy runs in the background and handles all routing automatically.

Can I switch between local and cloud mid-session? Yes. You can switch at any point, at the start of a session or in the middle of one. No restart required.

What model should I use for coding tasks? Any code-capable model deployed through OneInfer Edge’s Self Hosting will work. We recommend starting with smaller quantized models (GGUF format via Ollama) if you are on a machine with 8 to 16 GB of unified or dedicated VRAM.

Is there any latency compared to cloud copilots? Latency depends entirely on your hardware. A well-specified machine will generally be as fast or faster than a cloud endpoint, with zero network overhead and no queue. Read more about reducing AI inference latency.

Does my code stay private? Yes. In o mode, every prompt stays on your machine. No data is sent to any external server.

_Download OneInfer Edge, deploy your first local model in under five minutes, and switch your copilot to local AI with one click.

Explore the OneInfer blog for more guides on deploying and scaling AI in production._