Cisco’s GPUaaS platform on OpenShift SNO clusters enables secure, scalable AI experimentation. NVIDIA RTX PRO 6000 Blackwell GPUs are partitioned using MIG for mixed workload isolation. Time-slicing via device plugins allows concurrent inference tasks across 4 replicas per GPU. PostgreSQL serves as the centralized reservation database, while a cache daemon pre-stages heavy model artifacts. The control plane uses a Python-based reconciler to enforce capacity constraints through advisory locks.

Architecture breakdown: The system separates into scheduling (calendar-based reservations), control (continuous state convergence), and runtime (preconfigured ML environments). Kubernetes operators manage GPU allocation, with LVM StorageClasses handling 3.1TB NVMe volumes for persistent workloads. Air-gapped deployment workflows mirror production requirements, ensuring compliance in regulated industries.

Technical significance: By combining hardware partitioning (MIG) with Kubernetes resource management, the platform achieves multi-tenant isolation without sacrificing GPU utilization. Cost modeling integrates calendar time and hardware reservation costs, mirroring real-world enterprise constraints. OpenShift’s build artifacts enable in-cluster image creation, reducing deployment complexity.

Key takeaway: This lab-proven architecture provides a blueprint for enterprise AI infrastructure, demonstrating how on-premises GPUaaS can balance performance, security, and operational simplicity. Cisco UCS C845A’s specs (2x RTX PRO 6000, 127 CPU cores) validate the platform’s scalability for mid-sized AI teams.