Running AI models locally gives you complete control, privacy, and zero dependency on internet connectivity. This guide walks you through setting up Open WebUI with Ollama—a ChatGPT-like interface running entirely on your machine.

What You’ll Need

• Docker Desktop
• Ollama (local LLM runtime)
• About 15 minutes

Step 1: Install Docker Desktop

Docker provides the containerized environment for Open WebUI.

1. Download Docker Desktop: https://docs.docker.com/desktop/
2. Install and launch the application
3. Verify installation by opening a terminal and running: docker --version

Step 2: Install Ollama

Ollama runs the AI models locally on your machine.

1. Download Ollama: https://ollama.com/download
2. Install for your operating system
3. Verify installation: ollama --version
4. Pull a model (e.g., Llama 3.2): ollama pull llama3.2

Step 3: Launch Open WebUI

Run this single command to start Open WebUI:

docker run -d -p 3000:8080 \
  --add-host=host.docker.internal:host-gateway \
  -v open-webui:/app/backend/data \
  --name open-webui \
  --restart always \
  ghcr.io/open-webui/open-webui:main

What this does:

• Maps port 3000 on your machine to the container
• Enables communication with Ollama on your host
• Persists data between restarts
• Automatically restarts the container if it stops

Step 4: Access Your Interface

Open your browser and navigate to:

http://localhost:3000

Create an account (stored locally) and start chatting with your AI models.

Why This Setup?

For Program Managers:

• No vendor lock-in: Full control over your AI infrastructure
• Data privacy: Everything stays on your machine
• Cost predictable: No usage-based pricing
• Offline capable: Works without internet after initial setup
• Customizable: Add models and adjust configurations as needed

Troubleshooting

Can’t connect to Ollama? Ensure Ollama is running and the host gateway is properly configured.

Port 3000 already in use? Change -p 3000:8080 to -p 3001:8080 (or any available port).

Container won’t start? Check Docker Desktop is running and you have sufficient disk space.

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This setup provides a production-ready local AI assistant in under 15 minutes—no cloud dependencies required.