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How to Run Gemma 4 locally with OpenClaw

Last week, Gemma 4 was released. It is a highly capable and efficient model that can run directly on local machines, making it an excellent choice for developers interested in building AI applications without relying on cloud infrastructure.

This guide explains how to run Gemma 4 locally and connect it with OpenClaw in three straightforward steps.

Why Run Gemma 4 Locally?

Running models locally offers several advantages:

  • Greater data privacy and control
  • Reduced latency
  • No ongoing API costs
  • Flexibility to customize and integrate with local systems

When combined with OpenClaw, Gemma 4 can serve as the backbone for building local AI agents and automation workflows.

Step 1: Install Ollama

Begin by installing Ollama, a tool designed to simplify running large language models locally.

Download Ollama from:
https://ollama.com/download

The installation process is quick and supports most modern operating systems.

Step 2: Download Gemma 4 (26B A4B)

Next, download the Gemma 4 26B A4B model to your machine. This version provides a strong balance between performance and speed, making it well suited for local agent-based applications.

This step is optional, as the model can also be automatically downloaded during the next step.

Step 3: Launch OpenClaw with Gemma 4 via Ollama

Finally, launch OpenClaw configured to use Gemma 4 through Ollama.

During this process:

  • OpenClaw will be installed automatically if not already present
  • The Gemma 4 model will be downloaded if needed
  • The system will be configured and ready to run

This step completes the setup and enables you to start working with a fully functional local AI agent.

Conclusion

Setting up Gemma 4 locally with OpenClaw is a fast and straightforward process. In just a few minutes, you can move from installation to running a capable local AI system.

This setup provides a practical foundation for developing and experimenting with local AI agents while maintaining full control over your environment.

Next Steps

Once configured, you can begin:

  • Building and testing AI agents
  • Integrating with existing workflows
  • Customizing prompts and behaviors
  • Exploring advanced automation use cases