ai homelab

Hands-on, bash-first guide to running private, low-latency AI in a homelab on modest hardware: 3 copy/paste case studies - (1) local LLM chat via Ollama + Open WebUI, (2) offline RAG 'ask my docs' with local embeddings, (3) MLflow experiment tracking - plus apt/dnf/zypper commands, rootless containers, simple on-disk data, CPU-first defaults, and tips on backups, security, scaling, and automation.

Pragmatic, Bash-first guide to building a reliable, secure, reproducible AI homelab: why run local (privacy, latency, cost), a minimal OS foundation, containerized GPU stacks (Docker/Podman + NVIDIA toolkit), reproducible Python envs, data layout/sync/checksums, firewall/monitoring/automation, and copy‑paste commands for major distros—plus a vLLM server example and troubleshooting tips.

Guide to building an AI homelab that runs private LLMs on Debian/Ubuntu, Fedora/RHEL, or openSUSE, with Bash-first steps for CPU and GPU. Covers installing tools and containers, fastest path via Ollama (local REST API), DIY via llama.cpp, optional NVIDIA/AMD acceleration, serving and securing services, firewall and systemd, plus tips on models, storage, and monitoring—so you can stand up a local API fast and scale confidently.

A practical, Bash-first guide to building a private AI homelab on Linux: prepare OS and tools, use Docker or Podman, optionally enable GPU (NVIDIA with AMD/ROCm notes), run a local LLM via Ollama + Open WebUI and generate images with ComfyUI, or experiment in a Python venv. Includes hardening (firewall, services), monitoring, storage/backups, hardware sizing, and next steps like RAG, Compose scripting, scheduling, and benchmarking.