As LLMs become increasingly powerful and ubiquitous, the need to deploy and scale these models in production environments grows. However, the complexity of LLMs can make them challenging to run reliably and efficiently. In this talk, we'll explore how Kubernetes can be leveraged to run LLMs at scale. We'll cover the key considerations and best practices for packaging LLM inference services as containerized applications using popular OSS inference servers like TGI, vLLM and Ollama, and deploying them on Kubernetes. This includes managing model weights, handling dynamic batching and scaling, implementing advanced traffic routing, and ensuring high availability and fault tolerance.