Which two deployment patterns are MOST suitable for scaling agentic workloads on NVIDIA Infrastructure?

Together, D states “Containerized deployment with NIM (NVIDIA Inference Microservices)”; E states “Kubernetes orchestration with Horizontal Pod Autoscaling (HPA)”, so the answer covers both sides of the requirement instead of solving only the model or only the infrastructure layer. At production scale, the combination of Options D and E preserves separability between reasoning, state, tools, and runtime operations. Operationally, the design depends on independent scaling of agent components so embeddings, reranking, reasoning, and guardrails do not share one rigid capacity pool. NIM containers package optimized inference services, and Kubernetes HPA scales them. Bare metal and fixed VMs remove the elasticity needed for agent workloads. That is why the other options are traps: CPU-only or memory-only scaling signals rarely capture the saturation profile of GPU-backed LLM inference. For a production build, NIM microservices and the NIM Operator fit Kubernetes production operations; Triton provides serving primitives and Prometheus-exportable inference metrics for GPUs and models. It also creates clean evidence for audits, incident review, and root-cause analysis when behavior drifts.