How to Run a Local AI Assistant With Docker — From Setup to Live in 4 Hours

Running a local AI assistant with Docker involves enabling Docker Model Runner in Docker Desktop, pulling your chosen open weight model, starting the model runner to expose a local API endpoint, installing and configuring OpenClaw to connect to that endpoint, and then connecting OpenClaw to your chosen messaging platform. This workshop covers each of these steps with a live instructor.

Running a local AI assistant with Docker using Docker Model Runner requires a minimum of 16GB RAM for comfortable operation with a 7B parameter model. The instructor covers memory requirements for different model sizes and helps you choose the right model for your available hardware during the live session.

The workshop covers reliability configuration for your local AI assistant — including how to configure Docker Model Runner for automatic restart, how to monitor the assistant's health, and how to troubleshoot common issues that can cause the assistant to go offline.

Yes. Docker Model Runner runs alongside your other Docker containers without interference. Your local AI assistant shares the Docker infrastructure with your other projects. The instructor covers resource allocation to ensure your AI assistant does not impact your other Docker workloads.

Updating your local AI assistant involves pulling updated model versions through Docker Model Runner and updating the OpenClaw repository when new versions are released. The instructor covers the update process and how to handle updates without disrupting your running assistant.

Yes. Docker provides process isolation and network controls that make your local AI assistant setup secure. The workshop covers security configuration including allowlists, access controls, and network isolation to ensure your private AI assistant is properly secured.