NVIDIA Dynamo in 5 Minutes: Dynamo On Kubernetes for AI Inference

Your model endpoint works for a demo. What does it take to scale it for your team? In this episode of Dynamo in 5 Minutes, we walk through deploying NVIDIA Dynamo on Kubernetes, from defining your serving setup to sending requests to a live endpoint. You’ll learn: • How the Dynamo Operator creates and manages your serving setup • Two deployment paths: describe what you need with DGDR, or customize an existing deployment recipe with DGD • How requests move from an OpenAI-compatible frontend to inference engines such as SGLang, vLLM, or TensorRT-LLM • The full DGDR workflow in a live demo, from deployment spec to a working endpoint No prior experience with distributed inference required.. ➡️ Learn more: https://www.nvidia.com/en-us/ai/dynamo/ 📝 Tech blog: https://developer.nvidia.com/blog/tag/nvidia-dynamo/ 📥 Download: https://github.com/ai-dynamo/dynamo Chapters: 00:00 Introduction 00:31 Moving beyond a single model server 01:02 How the Dynamo Operator works 01:38 Two deployment paths: DGDR and DGD 02:45 Request flow and architecture 03:59 Demo: From deployment spec to live endpoint 04:52 Wrap-up