vCluster AI Conformance — v1.35 (Private Nodes)

CNCF Kubernetes AI Platform Conformance submission for vCluster with Private Nodes.

• Kubernetes version: v1.35
• Platform version: v0.32.0
• Submission: https://github.com/cncf/k8s-ai-conformance/tree/main/v1.35/vcluster-private-nodes
• k8s-conformance baseline: https://github.com/cncf/k8s-conformance/tree/master/v1.35/vcluster-with-private-nodes

What is vCluster with Private Nodes?

Private Nodes attach real physical Kubernetes nodes exclusively to a virtual cluster. This gives the vCluster dedicated GPU hardware for AI/ML workloads with node-level isolation — no other workload on the host cluster can schedule onto those nodes.

Files

File	Purpose
PRODUCT.yaml	CNCF AI conformance self-assessment — 8 MUST + 3 SHOULD items with evidence
README.md	This file

Conformance requirements covered

Category	Requirement	Level	Status
Accelerators	DRA support (ResourceClaim, DeviceClass)	MUST	Implemented
Accelerators	Driver & runtime management (NVIDIA GPU Operator)	SHOULD	Implemented
Accelerators	GPU sharing (MIG, time-slicing)	SHOULD	Implemented
Accelerators	Virtualized accelerators (vGPU)	SHOULD	Implemented
Networking	Gateway API for AI inference traffic management	MUST	Implemented
Scheduling	Gang scheduling (Volcano, NVIDIA KAI)	MUST	Implemented
Scheduling	Cluster autoscaling with GPU node groups (Auto Nodes)	MUST	Implemented
Scheduling	HPA with GPU custom metrics (DCGM + Prometheus Adapter)	MUST	Implemented
Observability	Accelerator metrics (DCGM-Exporter, OTel)	MUST	Implemented
Observability	AI service metrics (Prometheus discovery)	MUST	Implemented
Security	Secure accelerator access (node-level + DRA isolation)	MUST	Implemented
Operators	AI CRD operators (Kubeflow, KServe, webhooks)	MUST	Implemented

Re-certification

Run /k8s-ai-conformance-research annually when a new Kubernetes minor version is released to check for spec changes and dead evidence URLs before updating for the next cycle.