Local LLM Deployment Playbook

Automation and an operations guide for turning local hardware into a continuously available, OpenAI-compatible inference endpoint, provisioned reproducibly rather than by hand. Most of the written guidance covers the decisions that are easy to get wrong when you self-host models: choosing how aggressively to quantize against the quality it costs, keeping several models resident without exhausting memory, and pinning versions so an upstream change does not quietly alter behavior.