Run AI on Your Own Computer · Live · April 26

Run AI on Your Own Computer — Private, Free, and Fully Yours

Running AI on your own computer means your data never leaves your machine, you pay nothing ongoing, and you have complete control over which model you use. This live workshop shows you exactly how to set it up and build something genuinely useful with it.

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

Running AI on Your Own Computer

Why Running AI on Your Own Computer Is Now Practical in 2026

Docker Model Runner has made running AI on your own computer straightforward. Open weight models have become capable. OpenClaw provides the assistant layer. This workshop brings all three together into a complete private AI setup.

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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 Build Running AI on Your Own Computer

Six modules. AI running on your computer powering a deployed private 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

AI running on your own computer and powering a working private 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 to Run AI on Your Own Computer From a Docker Captain

Rami Krispin is a Docker Captain with expertise in 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 AI on Your Own Computer

Everything you need to know about getting AI running locally on your machine.

What do I need to run AI on my own computer? +

To run AI on your own computer you need Docker Desktop installed, a minimum of 16GB RAM, and basic Python familiarity. Docker Model Runner handles the model inference and OpenClaw provides the assistant layer. This workshop covers every step of the setup from scratch.

Can I run AI on my laptop or do I need a desktop? +

You can run AI on a modern laptop with 16GB RAM. Docker Model Runner is optimised for laptop use and the instructor covers model selection appropriate for laptop hardware during the live session. A dedicated GPU is not required for running AI on your own computer with this setup.

Will running AI on my own computer slow down other applications? +

Running a local LLM does use CPU and RAM. The instructor covers how to configure Docker Model Runner's resource limits so your other applications are not significantly affected while your AI assistant is idle. During active inference the model uses more resources momentarily.

How do I update the AI model running on my own computer? +

Updating the AI model running on your computer through Docker Model Runner is straightforward — you pull a new model image using the Docker CLI. The instructor covers model management including how to add new models, switch between them, and remove old ones.

Can I run AI on my own computer and access it from my phone? +

Yes. Since OpenClaw connects to WhatsApp or Telegram, you can interact with the AI running on your computer from your phone through those apps. The workshop also covers deploying to a VPS for always-on access even when your computer is switched off.

Is running AI on my own computer secure? +

Running AI on your own computer using Docker Model Runner is very secure from a data privacy perspective — all inference happens locally with no data leaving your machine. The workshop covers OpenClaw security configuration including allowlists and access controls to secure the assistant interface itself.