The AI landscape is dominated by cloud-dependent solutions that introduce data privacy risks, unpredictable costs, and vendor lock-in. But a new paradigm is emerging: private AI infrastructure that runs entirely on your hardware. In this seminar, Mario Iturrino — founder of fib0.ai — shares how his company built a distributed AI inference cluster using Apple Silicon hardware (M2 Ultra, M3 Ultra, M4 Max) with 1.4TB of unified memory, capable of running over 50 large language models, vision models, and embedding models entirely on-premises. Attendees will learn: how to evaluate whether private AI is right for their organization; the architecture behind distributed inference across commodity hardware using RDMA and Thunderbolt mesh networking; real-world case studies from healthcare, maritime logistics, and professional services; how a one-time hardware investment can eliminate per-token API fees and reduce AI operating costs by 70%; and strategies for deploying AI in air-gapped, regulated environments where data sovereignty is mandatory. Whether you're a CTO evaluating AI infrastructure options, an IT leader concerned about data governance, or a founder looking to build AI-powered products without cloud dependency, this session provides a practical, no-hype roadmap to private enterprise AI.