AI for Home Labs: Local LLM and AI Training Course

AI for Home Labs is a practical video training series focused on running, integrating, and using artificial intelligence on your own infrastructure.

Instead of looking at AI only as a cloud service, we explore how tools such as local large language models, AI-powered command-line utilities, and Model Context Protocol servers can become useful parts of a modern home lab.

The series includes hands-on training for self-hosting local LLMs so you can experiment with private AI models running on your own hardware. We look at the infrastructure behind these deployments, including virtualization, GPU resources, and the configuration needed to make local AI practical inside a home lab.

We also explore AI-assisted tools such as Gemini CLI and how command-line AI can fit into everyday administration, troubleshooting, coding, and automation workflows.

Another major area of the series is Model Context Protocol, or MCP. MCP servers provide a way for AI tools to interact with external systems and infrastructure. In a home lab, that opens the door to connecting AI with platforms such as Proxmox, Docker, Kubernetes, Terraform, Ansible, and other tools you may already be using.

My goal with this series is to move beyond simply chatting with an AI model and show how AI can become part of the infrastructure workflow itself.

Whether you want to run private LLMs, experiment with AI-assisted administration, or connect AI tools directly to your home lab services, this series provides practical examples you can reproduce in your own environment.

As the series grows, we will continue exploring local AI, GPU acceleration, AI coding tools, MCP servers, automation, infrastructure management, and new ways AI can be used alongside self-hosted systems.