Frequently Asked Questions
Common Questions About AI Assistant Homelab Setup
Common questions about the workshop, what to expect, and how to prepare.
What is the best homelab hardware for an AI assistant setup? +
For a homelab AI assistant setup, any machine with 16GB RAM or more works well. Mini PCs like the Intel NUC, Beelink, or similar compact devices are popular homelab AI choices for their low power consumption and always-on capability. Older workstation hardware with 32GB+ RAM allows for larger, higher quality models.
How do I set up remote access for my homelab AI assistant? +
The workshop focuses on WhatsApp and Telegram as the interface — these platforms provide secure remote access without requiring you to expose your homelab network to the internet. For direct access, the instructor briefly covers reverse proxy options during the deployment module.
What operating system should my homelab run for the AI assistant setup? +
Ubuntu Server or Debian are popular choices for Linux homelab AI assistant setups. For Mac Mini or Intel NUC running macOS or Windows, Docker Desktop provides a straightforward setup path. The workshop covers Docker configuration for both Linux servers and desktop operating systems.
Can I integrate my homelab AI assistant with my existing homelab services? +
Yes. OpenClaw's skills system can be extended to interact with other homelab services through Python-based skills. The instructor covers the skills architecture so you can build integrations with your existing homelab after the workshop.
How much RAM should I allocate for my homelab AI assistant Docker setup? +
Allocate at least 10GB RAM for Docker Model Runner with a 7B parameter model plus system overhead. On a homelab machine with 16GB total RAM this leaves about 6GB for the OS and other services. With 32GB RAM you can run larger models comfortably alongside other homelab services.
Will my homelab AI assistant slow down my other homelab services during inference? +
Inference can briefly spike CPU and memory usage. On dedicated homelab hardware this typically does not impact other services significantly. The instructor covers resource limit configuration for Docker Model Runner so inference does not starve other homelab services of resources.
