How to Run Gemma 4 with OpenClaw on Ollama (Free Local AI)

Provider Configuration

{
  "provider": "ollama",
  "baseUrl": "http://localhost:11434",
  "model": "gemma4:26b",
  "contextWindow": 131072,
  "reasoning": false,
  "temperature": 0.7,
  "maxTokens": 4096
}

Configuration Notes

• contextWindow: 131072 — matches Gemma 4's native 128K token context. Setting this correctly prevents OpenClaw from truncating context prematurely or sending oversized requests.
• reasoning: false — Gemma 4 does not have a native chain-of-thought reasoning mode like Claude or o1. Setting this to false prevents OpenClaw from sending reasoning-specific prompts that Gemma 4 would not handle correctly.
• temperature: 0.7 — a good default for agent tasks. Lower (0.3-0.5) for factual lookups, higher (0.8-1.0) for creative content.
• maxTokens: 4096 — maximum output length per response. Increase to 8192 for long-form content generation.

Step 4: Test the Connection

After updating the provider configuration, restart OpenClaw and send a test message through your preferred channel (Telegram, WhatsApp, or Slack).

What to Check

1. Response time: First response should arrive within 5-15 seconds on recommended hardware. If it takes longer than 30 seconds, check GPU offloading (see optimization section below).
2. Response quality: Ask the agent to summarize a recent email or draft a short message. The output should be coherent and well-formatted.
3. Memory persistence: Tell the agent a specific fact, then in a new message, ask it to recall that fact. Confirm OpenClaw's memory system is working with the local model.
4. Tool use: Ask the agent to perform a skill that requires tool calls (calendar check, file read). Verify Gemma 4 generates correct tool-call syntax.

If any of these checks fail, consult the Best Ollama Models for OpenClaw guide for troubleshooting steps specific to local model configurations.

Performance Optimization Tips

Running a 26B parameter model locally leaves less room for error than cloud APIs. These optimizations can significantly improve response speed and stability.

GPU Offloading

If you have a compatible GPU, ensure Ollama is using it. Check with:

ollama ps

The output should show your model loaded with GPU layers. If it shows CPU only, verify your GPU drivers are installed and Ollama can detect the GPU.

Context Window Tuning

While Gemma 4 supports 128K tokens, using the full context window consumes significant memory. If you are running low on RAM, reduce contextWindow to 32768 or 65536 in your OpenClaw config. Most agent conversations fit well within 32K tokens.

Concurrent Request Limits

Unlike cloud APIs, local models process one request at a time by default. If your OpenClaw agent handles multiple channels simultaneously, configure Ollama's OLLAMA_NUM_PARALLEL environment variable:

export OLLAMA_NUM_PARALLEL=2

Setting this to 2-3 allows Ollama to process multiple requests in parallel, though each additional parallel request increases memory usage.

Cost Comparison: Local vs Cloud

The financial case for running Gemma 4 locally is straightforward.

Over 12 months, a local Gemma 4 deployment saves $240-600 compared to cloud APIs. The trade-off is lower quality on complex reasoning tasks and the upfront hardware requirement. For operators who already have a capable machine, the savings are immediate.

For a deeper comparison of all local models compatible with OpenClaw, see Best Ollama Models for OpenClaw. For more on Gemma 4 capabilities beyond OpenClaw, see Gemma 4: Google's Open Model.

Frequently Asked Questions

Which Gemma 4 variant should I use with OpenClaw?

The recommended variant is gemma4:26b, the 26B Mixture of Experts model. It runs at approximately the same speed as the 4B parameter model because only a subset of experts activate per token, but delivers substantially higher quality output. It requires 16GB of RAM minimum (24GB recommended) and fits comfortably on a modern GPU with 16GB+ VRAM.

How much does it cost to run Gemma 4 locally with OpenClaw?

The total cost is $0 for software. Gemma 4 is open source under Google's permissive license, Ollama is free, and OpenClaw is free. The only cost is your hardware and electricity. Compared to cloud API costs of $15-40 per month for a typical OpenClaw deployment, running locally eliminates the largest ongoing expense entirely.

Can Gemma 4 on Ollama match Claude or GPT-4 quality for OpenClaw?

Not for complex reasoning or long-form generation. The gemma4:26b variant performs well for structured tasks like email triage, CRM updates, scheduling, and template-based responses. For tasks requiring deep reasoning, nuanced writing, or large context windows beyond 128K tokens, cloud models like Claude Sonnet or GPT-4o still produce better results. Many operators use Gemma 4 locally for routine tasks and fall back to cloud APIs for complex work.