How to Run Gemma 4 Locally With OpenClaw in 3 Steps

By . in Toolkit. Updated on .

Running a capable AI model locally does not have to turn into a weekend project.

Google DeepMind’s Gemma 4 models are light enough to run on local hardware, and with Ollama plus OpenClaw, going from “nothing installed” to “local AI agent running” is fairly quick.

Gemma 4 Ollama

No API keys. No cloud dependency. No account hopping just to test an agent on your own machine.

If the goal is the shortest path from zero to working, this is it.

Step 1: Install Ollama

Ollama handles the heavy lifting for local models: downloading them, serving them, and making them available to apps.

Grab it from the official download page, install it for your operating system, then make sure it is running. For a more complete walkthrough, Getting Started with Ollama is a useful companion.

Step 2: Pull Gemma 4 26B (Optional)

If the model is already decided, pull it first:

ollama pull gemma4:26b

This step is optional. OpenClaw can trigger the download automatically later if the model is missing.

The gemma4:26b model is a sensible starting point if the machine has enough headroom for local agent work. For a broader look at local multimodal setups, this guide to vision-enabled models in Ollama is also worth a look.

Step 3: Launch OpenClaw With Gemma 4

Now run:

openclaw --model ollama/gemma4:26b

That command handles the rest.

OpenClaw connects through Ollama, checks that the model is available, and starts the agent interface with Gemma 4 as the active model. Another useful setup example is How to Use OpenClaw with DeepSeek, which follows the same general idea with a different model.

At that point, the local AI agent is up and running.

Note: If gemma4:26b feels too heavy for the machine, switch to a smaller Gemma variant instead. The setup stays the same. Only the model name changes.

Thoughts

This setup sounds more complicated than it really is.

Install Ollama. Pick a Gemma 4 model. Launch OpenClaw with it.

Once it is running, you have a private local agent setup that is easy to test, tweak, and build on.

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