NVIDIA AICR Generates It. Rafay Runs It. Your GPU Clusters, Finally Under Control
Deploying GPU-accelerated Kubernetes infrastructure for AI workloads has never been simple. Administrators face a relentless compatibility matrix i.e. matching GPU driver versions to CUDA releases, pinning Kubernetes versions to container runtimes, tuning configurations differently for NVIDIA H100s versus A100s, and doing all of it differently again for training versus inference.
One wrong version combination and workloads fail silently, or worse, perform far below hardware capability. For years, the answer was static documentation, tribal knowledge, and hoping that whoever wrote the runbook last week remembered to update it.
NVIDIA's AI Cluster Runtime (AICR) and the Rafay Platform represent a new approach — one where GPU infrastructure configuration is treated as code, generated deterministically, validated against real hardware, and enforced continuously across fleets of clusters.
Together, they cover the full lifecycle from first aicr snapshot to production-grade day-2 operations, with cluster blueprints as the critical bridge between the two.
