# Validating GPU Platforms with Runtime AI Inventory via NVIDIA DSX NVIDIA integrates k8s-aibom into AICR to track AI workloads on Kubernetes clusters. - **Source:** [Nvidia Developer Blog](https://developer.nvidia.com/blog) - **Published:** 2026-11-11 - **Topic:** [technology](https://brifing.si/c/technology) - **Original article:** - **On Brifing:** https://brifing.si/s/480a17ae-0b05-492c-9a73-79f30960db4b ## Bridging Infrastructure Visibility Gaps NVIDIA has integrated the open-source k8s-aibom project into its AI Cluster Runtime (AICR) within the DSX AI Factory platform. This update addresses the challenge of tracking AI workloads on Kubernetes, where mutable image tags and independent application updates often obscure exactly which software artifacts are running on specific GPU-accelerated nodes. ## AICR and AIBOM Synergy AICR focuses on generating version-locked, reproducible deployment bundles for GPU-accelerated Kubernetes environments. Meanwhile, k8s-aibom operates at a higher level, observing pod APIs to emit CycloneDX 1.6 records. Together, they provide a comprehensive view of both the underlying infrastructure configuration and the specific AI workloads currently active within the cluster. ## Automated Recipe Generation Users can select k8s-aibom by default using stock recipes like h100-gke-cos-inference or opt in via the --runtime-inventory flag. These recipes ensure that hardware, OS, and Kubernetes components are configured consistently. By pinning k8s-aibom 1.6.2, AICR v1.2.0 establishes a validated, repeatable foundation for deploying complex AI inference workloads on GKE. ## Resource Usage Metrics Performance testing of the integration shows minimal overhead during steady-state observation. The controller consumes approximately 2.2 millicores of CPU and maintains a memory working set between 28 and 34 MiB. These metrics confirm that the inventory process remains lightweight while performing roughly four API-server requests per minute on the cluster. ## Namespace-Level Inventory Control Inventory tracking is strictly opt-in, managed by applying a specific label to Kubernetes namespaces. This design ensures that the controller only monitors designated areas of the cluster. By default, no secret access or external data sinks are configured, keeping sensitive workload information contained within the cluster environment during the inventory process. ## Workload Classification Capabilities The system automatically identifies and classifies AI workloads, such as vLLM inference deployments, by analyzing their specifications. It captures critical details including model names and container image information. Because the record is built from the workload spec, the AIBOM can be generated even before the associated pods are successfully running. ## Confidence Tiers and Evidence Unlike standard SBOMs that only track images, AIBOMs provide specific insights into serving runtimes and models. Each attribute is assigned one of four confidence tiers—declared, inferred, unresolved, or verified—accompanied by an evidence locator. This transparency allows security reviewers to understand exactly how the system reached its conclusions about the running software. ## Deployment and Verification Workflow The workflow involves generating a recipe, rendering it into a bundle, and deploying via tools like Helm, Flux, or Argo CD. Post-deployment, users can validate the installation using the aicr validate command. This phase checks the controller availability and ensures that CRDs report the storage version expected by the pinned chart. ## Current Integration Boundaries The current integration intentionally excludes signed runtime observations and automated correlation between deployment intent and observed state. While these features are technically feasible given the published SHA-256 hashes, they remain future development goals. The system currently focuses on providing accurate, point-in-time inventory rather than enforcing full cluster conformance or execution success. ## Strategic Importance for Governance This integration provides the missing link between infrastructure deployment and workload traceability in AI factories. By combining AICR’s reproducible configurations with k8s-aibom’s runtime visibility, organizations gain a standardized method for AI governance. This approach is essential for teams managing large-scale GPU clusters where manual tracking of diverse AI models is unsustainable. ## Sources covering this story - https://developer.nvidia.com/blog/validating-gpu-platforms-with-runtime-ai-inventory-via-the-nvidia-dsx-ai-factory/ — --- _Summary written by Brifing from the original reporting. Copyright in the original article belongs to its publisher; read the full piece at the link above._