Certifying Tenant GPU Kubernetes Clusters with NVIDIA Cluster Readiness Engine
For tenants of a neocloud, provisioning a GPU Kubernetes cluster is only part of the job. Before handing over newly provisioned cluster to a tenant, the provider needs confidence that the underlying GPU infrastructure is actually ready to run AI workloads.
Kubernetes nodes may report Ready, GPUs may appear as allocatable resources, and the NVIDIA GPU Operator may be healthy. But, none of these checks prove that GPUs across multiple nodes can communicate correctly or successfully execute distributed workloads.
This is the exact problem NVIDIA Cluster Readiness Engine (NVCRE) attempts to address.
NVCRE provides a Kubernetes-native framework for running GPU cluster certification tests. Rather than validating infrastructure solely through health checks, NVCRE executes real GPU workloads—including NCCL communication tests, GPU diagnostics, and distributed training workloads—and produces a certification result. For a neocloud, this can provide a powerful operating model: "Provision → Configure → Certify → Hand Off to Tenant"
In this blog, we’ll walk through a simple example using a two-node Kubernetes cluster with NVIDIA A10 GPUs and show how the same workflow can become part of a neocloud’s automated Kubernetes cluster delivery process.
