Docker Model Runner Tutorial · Live · April 26

The Docker Model Runner Tutorial That Goes From Setup to Working AI Assistant

Docker Model Runner lets you run powerful open weight LLMs locally on your own machine. This live tutorial teaches you to set it up, configure it, and connect it to OpenClaw to build a complete private AI assistant in 4 hours.

Sunday, April 26 9am to 1pm EDT

4 Hours Hands-on coding

Live Online Interactive

Workshop Details

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Date and Time

Sunday, April 26, 2026

9:00am to 1:00pm EDT

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Duration

4 Hours · Hands-on

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Format

Live Online · Interactive

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Includes

Certificate of Completion

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Privacy

100% Local · No Cloud Required

By Packt Publishing · Refunds up to 10 days before

About This Docker Model Runner Tutorial

Why This Docker Model Runner Tutorial Goes Beyond Basic Setup

Most Docker Model Runner tutorials show you how to pull a model and get a response in the terminal. This live tutorial goes further — connecting Docker Model Runner to OpenClaw and building a complete private AI assistant deployed to WhatsApp or Telegram.

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What is OpenClaw?

OpenClaw is the open-source personal AI assistant that went viral in early 2026 with 200K+ GitHub stars. It runs on your own devices and connects to WhatsApp, Telegram, Slack and more. No subscription. No data leaving your machine.

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What is Docker Model Runner?

Docker Model Runner is Docker's native feature for running large language models locally on your machine. It gives you an OpenAI-compatible API that OpenClaw uses as its AI brain — complete data privacy, no cloud costs.

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Why Combine OpenClaw and Docker?

OpenClaw gives you the assistant interface and messaging integrations. Docker Model Runner gives you the AI brain running privately on your machine. Together they create a production grade private AI assistant you fully own.

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Why Attend as a Live Workshop?

Setting this up from scattered documentation takes days of debugging. This live workshop gives you a complete guided build in 4 hours with a live instructor answering your questions. Packt has delivered 108 workshops worldwide.

Docker Model Runner Tutorial Curriculum

What This Docker Model Runner Tutorial Covers

Six modules. From Docker Model Runner setup to a fully deployed private AI assistant.

How OpenClaw Works

Understand the Gateway, channels, and skills architecture. Set up and configure OpenClaw locally from scratch.

Docker Model Runner Setup

Run and manage local LLMs using Docker Model Runner. Pull models, configure memory, and understand the OpenAI-compatible API.

Security and Privacy

Configure DM pairing, allowlists, sandbox mode, and proper access controls for your local AI deployment.

Connect to WhatsApp or Telegram

Deploy your AI assistant to real messaging platforms without sending data to any third party cloud service.

Scalable Architecture

Design an extensible assistant architecture. Add skills, configure personality, and set up proactive automation.

Production Deployment

Deploy your OpenClaw and Docker setup to a VPS for always-on availability running 24 hours a day.

What You Walk Away With

By the End of This Docker Model Runner Tutorial You Will Have

A fully configured Docker Model Runner setup powering a working private AI assistant.

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A fully functional local AI assistant running on your machine

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Docker Model Runner configured with your chosen LLM model

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OpenClaw connected to WhatsApp or Telegram

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Security and privacy configuration you can trust

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A reusable architecture for future AI assistant projects

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Certificate of completion from Packt Publishing

Your Instructor

Learn Docker Model Runner From a Docker Captain

Rami Krispin is a Docker Captain with deep expertise in production Docker deployments.

Rami Krispin

Workshop Instructor · April 26, 2026

Rami is a Senior Manager of Data Science and Engineering, Docker Captain, and LinkedIn Learning Instructor with deep expertise in building and deploying production AI systems. He guides you step by step from a blank terminal to a fully deployed private AI assistant — answering your questions live throughout the 4-hour session.

Frequently Asked Questions

Common Questions About Docker Model Runner

Everything you need to know about Docker Model Runner before getting started.

What is Docker Model Runner and what does it do? +

Docker Model Runner is Docker's native feature for running large language models locally on your own machine. It provides an OpenAI-compatible API endpoint so your applications can connect to a locally running LLM just as they would connect to the OpenAI API — but with no cloud dependency, no API costs, and complete data privacy.

Which LLMs can I run with Docker Model Runner? +

Docker Model Runner supports a wide range of open weight models including Llama, Mistral, Phi, Qwen, Gemma and others. In this tutorial you will learn to pull models, compare their sizes and capabilities, and select the right model for your hardware specifications.

How much RAM do I need to run Docker Model Runner? +

A minimum of 16GB RAM is recommended for a smooth Docker Model Runner experience. The instructor covers model selection appropriate for different hardware configurations during the live session — including options for machines with 8GB, 16GB, and 32GB of RAM.

Does Docker Model Runner work without a GPU? +

Yes. Docker Model Runner can run open weight models on CPU without a GPU. Performance is slower than GPU inference but perfectly usable for a personal AI assistant. The instructor covers both CPU and GPU configurations during this tutorial.

How does Docker Model Runner compare to Ollama? +

Both Docker Model Runner and Ollama run LLMs locally. Docker Model Runner is built directly into Docker Desktop making it the natural choice if you are already using Docker. It provides an OpenAI-compatible API, tight Docker integration, and is actively maintained by the Docker team. This tutorial focuses on Docker Model Runner as the backend for OpenClaw.

Is Docker Model Runner free to use? +

Yes. Docker Model Runner is included in Docker Desktop at no additional cost. The open weight models you run with it are also free. There are no usage fees, no API costs, and no subscriptions required.