ondevice inference

Shows how to build and run AI entirely offline on Linux. Covers selecting lightweight local models, installing dependencies without internet, and running inference with CLI tools, Python, and containers. Includes practical, step‑by‑step demos, GPU/CPU tips, caching and dataset handling, and scripts for automation—emphasizing privacy, reproducibility, and robust air‑gapped workflows.

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Breaks down running AI on Linux locally versus in the cloud: when to choose each, trade-offs in latency, privacy, offline use, scalability, and cost. Details GPU/driver needs (CUDA/ROCm), frameworks and runtimes, containers, and model sizing. Includes guidance for LLM inference, vision, and fine-tuning, plus hybrid workflows, cost modeling, and security/compliance considerations to help pick the right stack.