NVIDIA GPU Operator Government Ready

Qualify the actual workload and required platform features with campus IT, research-computing leadership, security and procurement. Ask whether the project requires specific storage or confidentiality integrations, which Kubernetes service is supported, and who will operate it. The value hypothesis is a supportable GPU service that meets explicit requirements, subject to component verification. Offer a compatibility and operating-readiness assessment rather than promising feature parity. Use the documented omissions to make scope visible early. This source cannot justify expected research productivity, reduced staffing, universal platform support, or a claim that every component has the same assurance status.

Build a versioned bill of materials and map each requested capability to an available component, its maintainer and distribution prerequisites. Resolve gaps before designing dependent workloads. Review privileged permissions, registry access and secret storage in the intended cluster. In an isolated validation environment, install the supported combination, run the customer's representative GPU workload, and test node replacement and an operator upgrade. Capture image digests and configuration so results can be reproduced. The proof-of-value decision should identify supported paths, exceptions and unavailable features explicitly, with a technical owner accepting dependencies outside the main supplier's support boundary.

Create installation and upgrade runbooks with ownership divided across the Kubernetes platform team, GPU specialist and security reviewer. Dependencies include subscriptions, registry access, distribution support and a maintained component manifest. Teach service users how to request GPU capacity and report failures without exposing credentials. Proposed acceptance criteria include workload rescheduling after node replacement, tested credential rotation, and recovery to approved component versions within the agreed service window. Keep change evidence for every exception. Delivery risks are support-matrix drift, unmanaged upstream dependencies, and approval assumptions that no longer match the installed platform.