Forking OpenClaw with a Custom LLM: A Local-Only Setup Guide

A developer on r/openclaw shared their experience forking OpenClaw to run entirely on local models, resulting in a fully customized AI gateway named JARVIS. The entire process was handled by their OpenClaw instance, Skippy, which used the qwen3.8-27 model to automate the fork, renaming packages, updating configs, and managing state directories. What took minutes, not hours, shows that building your own AI gateway is now accessible to individual developers.

Key Takeaways

• Full Local Inference: The fork runs with zero cloud dependency, using only local models.
• Customization Without Touching the Original: You can modify everything in the fork without affecting the initial OpenClaw installation.
• Multiple Instances: Two versions of OpenClaw now run side-by-side on an M2 Ultra, each with its own state dirs, both demanding GPU resources.

How the Fork Was Made

The process was simpler than expected. Skippy handled the heavy lifting, including:

• Renaming packages
• Updating configs
• Managing state directories

The result was a completely standalone gateway that runs independently of the original install.

Resource Conflicts

The developer notes that running two versions simultaneously on the same machine leads to GPU resource contention — both instances fight for the same GPU. While it works, managing resource allocation between multiple AI systems is an open challenge worth exploring.

Who This Is For

If you're a developer with a decent Mac (like the M2 Ultra) and local models, you can build your own AI gateway without a team or massive infrastructure. This approach gives you complete control and privacy.