Run LLMs With Docker Desktop · Live · April 26

How to Run LLMs Locally With Docker Desktop — Complete Workshop

Docker Desktop now includes Docker Model Runner — a native feature for running large language models locally. This live workshop shows you how to use it to power a complete private AI assistant connected to WhatsApp or Telegram.

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 Workshop

Why Docker Desktop Is Now the Easiest Way to Run LLMs Locally

Docker Desktop's integration of Docker Model Runner means you no longer need separate tools to run LLMs locally. If you have Docker Desktop, you have everything you need. This workshop shows you how to use it to build a complete private AI assistant.

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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.

Workshop Curriculum

What You Will Learn Running LLMs Locally With Docker Desktop

Six modules. From enabling Docker Model Runner 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 Workshop You Will Have

LLMs running locally in Docker Desktop 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 Desktop LLM Setup From a Docker Captain

Rami Krispin is a Docker Captain with deep expertise in Docker Desktop and local AI deployment.

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 Running LLMs Locally With Docker Desktop

Everything you need to know about using Docker Desktop for local LLM inference.

How do I run LLMs locally with Docker Desktop? +

Running LLMs locally with Docker Desktop uses the Docker Model Runner feature. You enable Model Runner in Docker Desktop settings, pull your chosen open weight model using the Docker CLI, and then use the local OpenAI-compatible API endpoint in your applications. This workshop covers every step of this process with a live instructor.

Which version of Docker Desktop includes Model Runner for running LLMs locally? +

Docker Model Runner is available in recent versions of Docker Desktop. The instructor covers the minimum required version and how to update during the first module of this workshop. If you already have a recent version of Docker Desktop, you likely already have Docker Model Runner available.

Can I run LLMs locally with Docker Desktop on an older laptop? +

Yes. Docker Model Runner can run smaller open weight models on machines with 8GB of RAM, though 16GB is recommended for a better experience. The instructor covers model selection appropriate for different hardware specifications during the live session.

How much disk space do I need to run LLMs locally with Docker Desktop? +

Open weight models vary in size from around 2GB to over 30GB depending on the model and quantisation level. For this workshop, the instructor recommends models in the 4GB to 8GB range as a good balance of quality and resource usage.

Is there a cost to running LLMs locally with Docker Desktop? +

No. Docker Desktop is free for personal use and Docker Model Runner is included at no additional cost. The open weight models you run are also free. Running LLMs locally with Docker Desktop has zero ongoing costs after your initial hardware investment.

How do I connect an application to LLMs running locally in Docker Desktop? +

Docker Model Runner exposes a local OpenAI-compatible API endpoint. Any application that supports the OpenAI API can connect to LLMs running in Docker Desktop by pointing to this local endpoint instead of the OpenAI API URL. In this workshop you will connect OpenClaw to this endpoint to build a complete private AI assistant.