AI Assistant VPS Deployment · Live · April 26

Deploy Your AI Assistant to a VPS — Always-On, Private, and Yours

VPS deployment gives your AI assistant always-on availability — responding to WhatsApp and Telegram messages 24 hours a day, even when your laptop is off. This live workshop covers the complete AI assistant VPS deployment using OpenClaw and Docker Model Runner.

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 VPS Deployment Is the Final Step for Your Private AI Assistant

Running your AI assistant locally works but stops when your laptop sleeps. VPS deployment gives you a private AI assistant that is always available — on your own server, with no cloud AI dependency, and responsive at any time of day or night.

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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

How to Deploy Your AI Assistant to a VPS

Six modules. Four hours. One working private AI assistant by the time you finish.

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

Concrete working deliverables — not just theory.

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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 AI Assistant VPS Deployment From a Docker Captain

Rami Krispin has deployed AI assistants to VPS servers in production environments.

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 AI Assistant VPS Deployment

Common questions about the workshop, what to expect, and how to prepare.

What VPS specs do I need for an AI assistant deployment? +

A VPS with 16GB RAM and 2 to 4 CPU cores is recommended for running a 7B parameter model. This typically costs $20 to $40 per month depending on the provider. The instructor covers VPS provider recommendations and how to evaluate specs during the workshop.

Which VPS providers work best for AI assistant deployment? +

Popular VPS providers for AI assistant deployment include DigitalOcean, Hetzner, Vultr, and Linode. Hetzner offers particularly good value for RAM-heavy deployments in Europe. The instructor covers provider comparison during the deployment module.

How do I set up Docker Model Runner on a VPS? +

VPS servers typically run Linux without Docker Desktop's GUI. The workshop covers installing Docker Engine on Ubuntu, enabling Docker Model Runner through the Docker Engine configuration, and pulling your chosen model — all from the command line without a GUI.

How do I secure my AI assistant VPS deployment? +

VPS security includes: SSH key authentication, firewall configuration to restrict unnecessary ports, Docker network isolation, OpenClaw allowlist configuration, and regular system updates. The workshop covers security hardening appropriate for a public-facing VPS deployment.

Can I deploy my AI assistant to a VPS I already use for other services? +

Yes — if your VPS has sufficient free RAM. Sharing a VPS between your AI assistant and other services is possible with careful resource allocation. The instructor covers Docker resource limit configuration to prevent your AI assistant from impacting other services.

How do I move my AI assistant from my laptop to a VPS? +

The migration process involves copying your OpenClaw configuration to the VPS, pulling your chosen model on the VPS using Docker Model Runner, and updating your WhatsApp or Telegram connection to point to the VPS instance. The instructor covers this migration process during the deployment module.