Generative language models are rapidly becoming central to a growing number of businesses worldwide. However, with only a few dominant providers offering language model services, the potential for efficient and effective self-hosting is increasingly at risk. This tutorial explores practical methods for serving large language models (LLMs) in production environments, with a focus on self-hosted deployments. The talk will cover a full deployment pipeline, from acquiring the model to selecting the appropriate server and framework stack. The session will examine the architecture of an end-to-end generative AI serving system and discuss popular frameworks used in inference, such as vLLM, TensorRT, and Triton. Through detailed examples and hands-on insights, attendees will gain the tools and understanding needed to make informed decisions about serving LLMs at scale, independently and efficiently.