Build a Self-Hosted AI Assistant With Docker — The Complete Setup

Docker Model Runner integrates LLM inference directly into Docker Desktop, OpenClaw runs as a Python service that connects to it, and the entire stack can be deployed consistently across different machines. Docker provides reproducibility, resource management, and clean process isolation making it the most reliable platform for a self-hosted AI assistant in 2026.

The workshop uses Docker Model Runner and standard Docker CLI commands for the core setup. Docker Compose patterns are briefly covered for those who want to orchestrate multiple services. The instructor helps you choose the right Docker approach for your specific deployment scenario.

Yes. Docker Desktop runs on Mac, Windows, and Linux making the self-hosted Docker AI assistant portable across operating systems. The workshop covers setup on Mac and Windows specifically. The VPS deployment module covers Linux server deployment for always-on hosting.

The Docker Model Runner takes minimal space. Open weight models range from 2GB to 8GB depending on size. OpenClaw itself is lightweight. Plan for 10 to 20GB of total disk space for a comfortable self-hosted Docker AI assistant setup with one or two models installed.

Yes. Docker Model Runner makes model updates straightforward — pull the new model version using the Docker CLI. OpenClaw updates are handled through standard git pull and restart. The instructor covers the update workflow during the workshop so your self-hosted setup stays current with minimal effort.

Yes. Deploy your self-hosted Docker AI assistant on a VPS with adequate RAM to handle concurrent inference requests. OpenClaw manages multiple simultaneous conversations and the Docker infrastructure handles resource allocation. The instructor covers multi-user configuration and VPS sizing during the deployment module.