gpu-workload

To deploy GPU inference services on Kubernetes, you need to enable Dynamic Resource Allocation (DRA) and install the NVIDIA GPU Operator. This allows automated allocation and monitoring of GPU workloads on compatible clusters like DGX Spark.

Yes, running end-to-end GPU workloads on DGX Spark requires NVIDIA GPU Operator 25.10.0+. You also need DRA support in Kubernetes 1.32+ and compatible cluster tooling to ensure proper scheduling and deployment.

The UMA memory architecture on DGX Spark affects GPU time-sharing by requiring specific scheduling considerations for optimal performance. Proper configuration ensures efficient time-slicing and resource utilization across shared GPU workloads.

The best way to automate batch processing jobs on GPU clusters is by integrating the NVIDIA GPU Operator with DRA. This streamlines deployment, reduces setup errors, and monitors GPU utilization for inference and batch tasks.

Yes, you can monitor GPU utilization for inference services in Kubernetes by deploying the NVIDIA GPU Operator. It provides the necessary tooling to track and manage end-to-end GPU workload performance on DGX Spark clusters.

Kubernetes 1.32+ requires DRA for GPU workload scheduling to enable dynamic allocation of hardware resources. This replaces older device plugin mechanisms, allowing flexible and automated GPU sharing across time-sliced workloads.