Complete procedures with a way back.

Run one pinned Qwen3-4B Q4_K_M GGUF through llama.cpp, Ollama, and LM Studio on the same machine, normalize the prompt and generation contract, measure cold and warm behavior, blind-score identical outputs, and choose a runner from control, automation, UX, operations, and workload evidence rather than reputation.

Select a licensed, checksum-backed GGUF and measured quantization, build llama.cpp from a pinned commit, size CPU/GPU resources, validate metadata and quality, isolate llama-server behind a trusted proxy, benchmark real workloads, and roll back the complete binary/model bundle.

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.