Edge LLM Inference in Production: Running Local Models Reliably with GGUF, mmap, and CPU/GPU Offloading
A systematic guide to deploying open-weight LLMs on local workstations, edge nodes, and internal servers. Covers GGUF quantization selection, mmap memory-mapped loading, CPU/GPU layer offloading tuning, service governance, and a production launch checklist to elevate edge inference from 'it runs' to 'operationally sound'.