How to Deploy NVIDIA Dynamo on Kubernetes for Distributed LLM Inference

This article provides a comprehensive tutorial for deploying NVIDIA Dynamo 1.3.0 on Kubernetes to manage distributed LLM inference. It explains why Dynamo is needed over standalone inference servers, detailing its Kubernetes-native control plane with features like prefill/decode separation, KV-cache-aware routing, topology-aware placement, and autoscaling. The guide walks through building a vLLM disaggregated deployment, configuring NIXL KV-transfer, and integrating with Gateway API. It also addresses production concerns such as shared model caches, distributed tracing, upgrades, and rollback, emphasizing that Dynamo coordinates engines like vLLM and TensorRT-LLM rather than replacing them.