Complete procedures with a way back.

Build a repeatable capacity-and-quality matrix for local GGUF models instead of choosing from a model leaderboard or a vendor logo. Inventory the real machine, calculate a safe memory envelope, pin Qwen3 4B, 8B, and 14B candidates, run llama.cpp b9637 with the appropriate CPU, Metal, CUDA, ROCm, or Vulkan backend, and select the smallest candidate that passes the workload at the required concurrency.

Integrate NVIDIA GPUs with Docker from a pinned driver/toolkit/CUDA matrix, assign devices by UUID or CDI, harden non-root workloads, monitor hardware with DCGM, and rehearse daemon-aware upgrades and rollback.

Deploy a pinned vLLM release behind a trusted TLS and authentication proxy, size GPU and KV-cache capacity from real workloads, isolate artifacts and listeners, verify quality and overload behavior, monitor the engine, and rehearse complete-bundle rollback.