home lab

A step-by-step guide to run modern AI at home with Kubernetes: create a single-node cluster (kind for CPU, k3s for GPU), deploy Ollama for local LLMs, optionally add Open WebUI, and enable NVIDIA via container toolkit/device plugin; expose with NodePorts, persist models on a PVC, and operate like production with ingress/TLS, rollouts, monitoring, and backups—private, cost-efficient, reproducible, and cloud-bill free.

Build a private, cloud-free AI home lab on Linux. This weekend-ready guide provides apt/dnf/zypper installs and four projects: run a local LLM with llama.cpp, transcribe audio offline with whisper.cpp, create semantic search using Qdrant + SentenceTransformers, and serve an AI API via llama.cpp + systemd. Covers hardware needs, tiny-model starts, optional GPU accel, monitoring, security, and ways to extend or chain the tools.

This guide shows how to wire local LLMs into Bash for your homelab using Ollama: install essentials, add a tiny ai.sh wrapper, and drop in three practical automations—generate/validate Nginx configs, triage logs into strict JSON, and craft daily health reports. It emphasizes privacy, low cost, and safety (review/validate, JSON-first, redaction, caching) and offers next steps to iterate and standardize your tooling.

Turn your Linux homelab into a private, scriptable AI stack with four copy-paste projects: run a local LLM API (llama.cpp), transcribe audio offline (faster-whisper), detect objects on IP camera frames (YOLOv8), and flag syslog anomalies (Isolation Forest). Includes apt/dnf/zypper installs, Bash snippets, CPU-first with optional GPU, plus tips on security, resource tuning, caching, and automation via cron/systemd.