One of my most rewarding experiences was helping a semiconductor manufacturing client run GPU workloads efficiently in a private cloud. The client struggled with statically assigned GPUs, resulting in significant resource underutilization.
What Changed with VCF 5.2.1: VCF 5.2.1 offered improved support for GPU resource pools and profiles. We deployed a workload domain dedicated to AI/ML workloads with GPU pass-through and NVIDIA vGPU integration. Tanzu Kubernetes Grid was used to support containerized pipelines, with Kubernetes clusters consuming GPU profiles via VM Class Definitions (https://docs.vmware.com/en/VMware-vSphere/8.0/vmware-vsphere-with-tanzu/GUID-B95EC2BC-9B89-4DAF-8D4B-46F4383AF76B.html).
TAM Engagement: I helped them create a vGPU-backed service catalog in VMware Cloud Director (VCD), exposing GPU profiles as tenant-consumable services with quotas, policies, and chargeback tracking.
Aria Operations for Applications and Aria Operations for Logs were used to monitor resource trends, abnormal usage patterns, and potential bottlenecks.
Impact:
- 80%+ GPU utilization across workloads
- 50% faster deployment of AI training pipelines
- Real-time visibility into per-tenant GPU usage and cost
Reference Documentation:
- vGPU and Tanzu Integration: https://docs.vmware.com/en/VMware-vSphere/8.0/vmware-vsphere-vcenter-server/GUID-E697D99F-13CD-4D6E-B143-9E5DFF3F9AC0.html
- GPU Profiles in VCD: https://docs.vmware.com/en/VMware-Cloud-Director/10.4/VMware-Cloud-Director-Administrators-Guide/GUID-7B9189E9-7403-4E60-A2E6-E3F92B56BA9B.html
