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A comprehensive production guide for self-hosting LLMs covering the full stack: hardware selection (H100, A100, RTX 4090, B200) with VRAM sizing math, quantization trade-offs (AWQ vs GPTQ vs GGUF), OS-level tuning for inference, Docker/NVIDIA Container Toolkit setup, inference engine comparison (vLLM, TGI, TensorRT-LLM, llama.cpp, Ollama), and observability pipelines using Prometheus and Grafana. Includes a cloud vs on-prem cost model showing break-even at ~10M tokens/day, production-ready code for a vLLM server with FastAPI auth/rate-limiting wrapper and Node.js client, and a detailed pre/post-deployment checklist. Targets platform teams and DevOps engineers with existing GPU and containerization experience.