Run LLMs Locally With Docker Desktop · April 26

How to Run LLMs Locally With Docker Desktop — the Right Way

Docker Desktop now includes native LLM support through Docker Model Runner. This live workshop shows you how to use it correctly — from enabling the feature to running a complete private AI assistant connected to WhatsApp or Telegram in 4 hours.

Sunday, April 26 9am to 1pm EDT

4 Hours Hands-on coding

Live Online Interactive

Workshop Details

📅

Date and Time

Sunday, April 26, 2026

9:00am to 1:00pm EDT

⏱

Duration

4 Hours · Hands-on

💻

Format

Live Online · Interactive

🎓

Includes

Certificate of Completion

🔒

Privacy

100% Local · No Cloud Required

By Packt Publishing · Refunds up to 10 days before

About This Workshop

Why Docker Desktop Is the Easiest Way to Run LLMs Locally in 2026

Docker Model Runner integrates directly into Docker Desktop — meaning if you already have Docker installed, you already have everything you need to run LLMs locally. No additional tools. No complex setup. This workshop shows you how to use it to its full potential.

🖥

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.

🐳

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.

🔗

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.

🎯

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 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 complete private AI assistant.

✓

A fully functional local AI assistant running on your machine

✓

Docker Model Runner configured with your chosen LLM model

✓

OpenClaw connected to WhatsApp or Telegram

✓

Security and privacy configuration you can trust

✓

A reusable architecture for future AI assistant projects

✓

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.

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 enable Docker Model Runner in Docker Desktop? +

Docker Model Runner can be enabled in Docker Desktop settings under the Features in Development section. The instructor covers the exact steps to enable it and verify it is working correctly during the first module of this workshop.

What Docker Desktop version do I need to run LLMs locally? +

Docker Model Runner requires a recent version of Docker Desktop. The instructor covers the minimum required version and how to update during the live session. If you have Docker Desktop installed and keep it updated, you likely already have Docker Model Runner available.

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

Intel-based Macs can run LLMs locally with Docker Desktop, though performance is better on Apple Silicon machines. The instructor covers hardware-appropriate model selection for different Mac configurations during the workshop.

Does running LLMs locally with Docker Desktop affect my other Docker containers? +

Docker Model Runner runs alongside your existing Docker containers. You can configure resource limits to ensure your locally running LLM does not impact your other Docker workloads. The instructor covers resource configuration during the workshop.

How do I know which LLM to run locally with Docker Desktop? +

The instructor evaluates multiple models during the workshop and helps you select the best one for your hardware. Key factors are model size, RAM requirements, and response quality for personal assistant tasks. You will leave with a clear understanding of which model works best for your specific machine.

Is there a UI for managing LLMs running locally in Docker Desktop? +

Docker Desktop has a GUI section for model management in newer versions. The workshop covers both the GUI and CLI approaches for managing your local LLMs through Docker Model Runner so you have flexibility in how you interact with your local AI setup.