Saylor Innovations Guide Library
Local Private-AI Deployment Guide. A procedure for sizing and securing a local Ubuntu AI deployment: mapping the real privacy/data-flow boundary first (local inference doesn't make retrieval, plugins, telemetry, or backups local), inventorying actual usable VRAM/RAM with a safety reserve before picking a model, installing as a versioned pinned service rather than an ad hoc shell launch, benchmarking on representative tasks (not just tokens/sec), binding interfaces to loopback with…
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