Running your AI assistant on a VPS server means it is always available — responding in WhatsApp and Telegram 24 hours a day, 7 days a week, from your own private server with no cloud AI dependency. This live workshop shows you how to set it up.
By Packt Publishing · Refunds up to 10 days before
A VPS server AI assistant combines always-on availability with complete ownership. Your AI model runs on your server, your conversations stay on your infrastructure, and your assistant responds at any time without needing your laptop to be awake.
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.
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.
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.
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.
Six modules. Four hours. One working private AI assistant by the time you finish.
Understand the Gateway, channels, and skills architecture. Set up and configure OpenClaw locally from scratch.
Run and manage local LLMs using Docker Model Runner. Pull models, configure memory, and understand the OpenAI-compatible API.
Configure DM pairing, allowlists, sandbox mode, and proper access controls for your local AI deployment.
Deploy your AI assistant to real messaging platforms without sending data to any third party cloud service.
Design an extensible assistant architecture. Add skills, configure personality, and set up proactive automation.
Deploy your OpenClaw and Docker setup to a VPS for always-on availability running 24 hours a day.
Concrete working deliverables — not just theory.
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
Rami Krispin runs AI assistants on VPS servers in production environments.
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.
You do not need to be an expert. You do need the basics.
Common questions about the workshop, what to expect, and how to prepare.
The minimum practical VPS spec for running a capable AI assistant is 16GB RAM and 2 CPU cores. This is sufficient for a 7B parameter model at 10 to 20 tokens per second response speed. For better performance, 32GB RAM and 4 cores provides a noticeably faster and more responsive AI assistant experience.
A VPS with 16GB RAM typically costs $20 to $40 per month depending on the provider and location. Hetzner Cloud offers some of the most competitive pricing with 16GB RAM instances available from around $15 per month in European data centers.
Basic Linux command line familiarity is helpful. You need to be comfortable with SSH connections, running commands, and editing configuration files in a terminal. The workshop covers every command you need — the instructor explains each step without assuming advanced Linux expertise.
Some VPS providers offer GPU-enabled instances for significantly better inference performance. The workshop covers CPU-based deployment which works well for personal use. The instructor briefly covers GPU VPS options during the deployment module.
The workshop covers monitoring your VPS AI assistant — checking Docker container health, monitoring memory usage, watching inference logs, and verifying the messaging platform connection. Simple monitoring using standard Linux tools is covered to ensure you can detect and resolve issues quickly.
A VPS is infrastructure you rent and control — your AI model runs on that server with no data going to any AI provider. A cloud AI service like OpenAI processes your data on their infrastructure regardless of what server your application runs on. Running on your own VPS with Docker Model Runner keeps AI processing entirely within your controlled infrastructure.
4 hours. Live instructor. AI assistant running on your VPS server by the end. Seats are limited.
Register Now →Sunday April 26 · 9am to 1pm EDT · Online · Packt Publishing