How to Deploy NVIDIA NIM Microservices on Kubernetes with the NIM Operator

This article provides a comprehensive tutorial on deploying NVIDIA NIM microservices on Kubernetes using the NIM Operator. It covers the operational challenges of running inference services on Kubernetes, including GPU driver installation, model caching, health probes, scaling, and upgrades. The tutorial details a step-by-step implementation path: building a supported Kubernetes and GPU foundation, installing the GPU Operator and NIM Operator, creating NGC secrets, populating a NIMCache, and deploying a NIMService. It emphasizes production considerations like client authentication, TLS, rate limiting, and endpoint protection via ingress controllers or API gateways. The goal is to leave readers with validation commands, rollback procedures, and troubleshooting guidance for reliable, GPU-accelerated inference in Kubernetes.