Tired of monthly AI subscriptions and privacy concerns? You need a way to run local coding models without sending your proprietary code to a third-party cloud.
If you are working on sensitive projects, the risk of data leaks is a constant headache. How about a workflow that stays entirely within your control? Used tools like Claude Code? Nanocoder? Opencode is basically the same Agentic Test Harness wrapper around an LLM, but in this case it’s open source and can use any LLM model you like including local ones.
The Privacy Problem in Modern Development
Using standard cloud LLMs means uploading your codebase to external servers every time you ask for a refactor or a bug fix. For many developers and enterprises, this potentially security vulnerability is a deal breaker.
Traditional AI tools are convenient, but they lack the sovereignty required for high-stakes engineering. This is why moving your intelligence layer to your local hardware is becoming the new standard.
The Solution: OpenCode.ai and LM Studio
Enter OpenCode.ai, an open-source powerhouse that can be configured to use custom API endpoints. By pairing it with LM Studio, you turn your own machine into a private AI engine. This is aimed squarely are replacing Claude Code.
This setup allows you to leverage high-performance models without any internet dependency. It is the same philosophy behind why tools like this are now easy to make with AI it puts the power back in your hands.
You will need a reasonable amount of GPU VRAM, which has sky rocketed in price lately (2026), you will want at least a 5080 which has 16GB of VRAM or better … or a unified chipset like a Mac M4 with 64+ RAM or a AMD AI CPU with again 64GB+ VRAM (128GB would be better). With the right Mac or AMD setup you can share most of the system memory with the inbuilt GPU and run decent local models.
Set your expectations accordingly though, as cloud models are hundreds of billions of parameters in size, you’ll be running models from 14-70 billion parameters on a local setup with 16 – 128GB of (V)RAM. You will need to also breakup your tasks into smaller prompts, as the local models will be less capable at following complex tasks with lots of steps.
Step-by-Step Implementation
1. Set up LM Studio
Download and launch LM Studio on your local machine. Use the search bar to find a model optimised for coding, such as Qwen 3.6 or Gemma 4, and download it.
2. Start the Local Server
Navigate to the Local Server tab in the LM Studio sidebar. Once your model is loaded, click the button to Start Server. Take note of the URL provided, which is typically http://localhost:1234.
3. Configure OpenCode.ai
Open your OpenCode.ai settings and locate the API configuration section, select LM Studio as your provider. Replace the default OpenAI endpoint with your local address. Ensure you append /v1 to the end of the URL so the paths align correctly.
The Results: Total Control
Once configured, you will experience effectively zero cost per token and total local privacy. Your code remains on your machine, and your development environment is yours again.
You now have a professional-grade coding assistant that is as secure as it is powerful.
Stop paying for tokens and start owning your intelligence.
This is a great way to really learn how to optimise prompts as well, token prices are going up so making small precise tasks even for Cloud AI models is soon going to be much more important!