The Complete Docker Local LLM Setup Tutorial — From Install to AI Assistant

This tutorial covers the complete Docker local LLM setup from start to finish — including Docker Desktop installation and configuration, enabling Docker Model Runner, pulling and configuring open weight models, setting up the local API endpoint, connecting OpenClaw as the assistant layer, integrating with WhatsApp or Telegram, and deploying to a VPS for always-on availability.

No. This Docker local LLM setup tutorial is designed for Python developers without prior Docker experience. The instructor covers Docker concepts as needed throughout the session, focusing on the practical setup rather than Docker theory.

The most commonly problematic aspects of Docker local LLM setup are Docker networking configuration, API endpoint accessibility, memory allocation for models, and connecting external applications like OpenClaw to the local model. This tutorial addresses all of these with step-by-step guidance and live troubleshooting.

This tutorial covers verification steps at each stage of the setup. You will learn to test Docker Model Runner directly, verify API endpoint accessibility, confirm model inference is working, and validate the OpenClaw connection before moving to messaging platform integration.

Yes. The Docker local LLM setup you complete in this tutorial uses a standard OpenAI-compatible API endpoint. Any application that supports the OpenAI API can use this local setup as a drop-in replacement — making it useful for any project where you want local LLM inference.

Yes. The final module of this tutorial covers deploying your Docker local LLM setup to a VPS server for production use — with always-on availability, proper resource configuration, and security best practices. You leave this tutorial with a setup suitable for real production use.