vLLM, an open-source inference and serving engine for large language models originally built at UC Berkeley, is presented as a solution for running open-weight models at production scale, addressing gaps left by tools like Ollama. The tutorial outlines a 12-step process to install vLLM, launch an OpenAI-compatible server, tune hardware, and deploy with Docker and Kubernetes, which can be completed in about 90 minutes. The guide covers prerequisites, VRAM requirements, creating a Python virtual environment, installing vLLM, verifying GPU access, downloading models from Hugging Face, launching the API server, sending chat completion requests, tuning GPU memory and batching, enabling multi-GPU tensor parallelism, quantizing models, containerizing with Docker, setting up a reverse proxy with HTTPS, monitoring throughput and latency, and deploying to Kubernetes for auto-scaling. It also includes a complete working project with a FastAPI chat service, common pitfalls, troubleshooting for 8 common errors, and advanced production tips. The article notes that vLLM is described on GitHub as a high-throughput and memory-efficient inference and serving engine for LLMs, and it addresses frequently asked questions about GPU requirements, commercial use, CPU-only operation, differences from Ollama or llama.cpp, Windows support, VRAM needs, compatibility with non-Hugging Face models, API drop-in replacement status, model switching, and handling excess requests.