local machine learning

AI without internet is practical on Linux. This guide shows how to stage models and wheels, verify checksums, and move them offline, then runs four complete workflows: llama.cpp LLM chat, Vosk speech-to-text, Tesseract OCR, and ONNX Runtime image classification. It covers privacy, uptime, cost benefits, hardware/storage/licensing constraints, plus an offline-first checklist and performance tips.

A practical guide to building a private, fully offline AI toolbox on Linux: why on-device AI improves privacy, cost, and latency; one-time setup per distro (apt/dnf/zypper); run local LLMs with Ollama or llama.cpp; offline speech-to-text via whisper.cpp; RAG Q&A over your documents using Chroma + sentence-transformers + Ollama; optional OCR with Tesseract; plus scripts, model/quantization choices, and performance/troubleshooting tips.