Running Gemma 4 Locally with Ollama and Docker

The Shift Toward Local Frontier Intelligence

For modern agencies, the reliance on cloud-based LLMs often introduces concerns regarding data sovereignty and recurring overhead. With the recent release of Gemma 4, Google has provided a solution that rivals proprietary models while remaining small enough to run on local hardware. Leveraging Ollama as your local runtime allows you to harness this power without sending sensitive information over the wire.

Gemma 4 introduces several architectural improvements, including the 26B Mixture of Experts (MoE) model, which only activates 3.8B parameters during inference. This results in incredibly low latency while maintaining the reasoning capabilities of a much larger dense model. This is a massive win for agencies building AI agents that require fast, private, and reliable inference.

Step 1: Installing the Ollama Runtime

Ollama serves as the bridge between the raw model weights and your applications. It handles quantization and memory management automatically.

For macOS and Linux:

Open your terminal and execute:

curl -fsSL https://ollama.com/install.sh | sh

Pulling Gemma 4 Models:

Google offers Gemma 4 in various sizes. For most agency workflows, the 26B MoE or the 31B Dense model is ideal. To download the model, run:

ollama pull gemma4:26b

Step 2: Scaling with Docker

For agencies that require a reproducible environment across development teams, Docker is the standard. Containerizing Ollama ensures that your LLM infrastructure is isolated and easy to manage.

Docker Compose Configuration:

Using a Compose file allows you to manage persistent storage and configure GPU passthrough for NVIDIA hardware.

services:  
  ollama:  
    image: ollama/ollama:latest  
    container_name: ollama-gemma-server  
    ports:  
      - "11434:11434"  
    volumes:  
      - ollama_storage:/root/.ollama  
    deploy:  
      resources:  
        reservations:  
          devices:  
            - driver: nvidia  
              count: all  
              capabilities: [gpu]  
    restart: unless-stopped

volumes:  
  ollama_storage:

Launch the service with docker compose up -d. This creates a stable API endpoint on port 11434 that can be shared across your local network.

Step 3: Advanced Agent Orchestration with OpenClaw

To move beyond simple chat, agencies are turning to OpenClaw (formerly known as Clawdbot). OpenClaw is a personal AI assistant platform that bridges messaging services like WhatsApp and Slack to local AI models.

By integrating OpenClaw with Ollama, you create a private AI workforce. You can launch it directly through the Ollama ecosystem:

ollama launch openclaw --model gemma4:26b

This command configures the gateway and opens a terminal interface where your agents can read files, run code, and execute multi-step research tasks using skills like valyu-search.

The Pro Hack: Using Claude Code for Free

One of the most powerful developments for AI agencies in 2026 is the ability to use Claude Code for free by routing it to local models. Since Claude Code expects an Anthropic-compatible API, we use a proxy like LiteLLM to translate the calls to your local Gemma 4 instance.

1. Install LiteLLM: pip install litellm
2. Start the Proxy: litellm --model ollama/gemma4:26b --port 4000
3. Configure Claude Code: Set your environment variables to point to the proxy instead of the cloud.

export ANTHROPIC_BASE_URL=http://localhost:4000  
export ANTHROPIC_API_KEY=ollama

Now, when you run claude in your terminal, the agent will use your local hardware for reasoning. This allows your team to leverage a world-class coding assistant without a per-token cost or privacy leaks.

Why This Matters for Agencies

Running local LLMs is no longer a niche experiment. It is a strategic necessity for agencies that prioritize security and efficiency. By deploying Gemma 4 through Ollama and Docker, and augmenting it with OpenClaw, you gain a localized powerhouse capable of multimodal understanding and long-context reasoning. This setup provides a robust, cost-effective foundation for the next generation of AI-driven app development.