As AI, machine learning, and data analytics workloads become more compute-hungry, IT teams are under pressure to deliver high-performance GPU infrastructure without compromising scalability or manageability. Enter VMware vSphere 9.0—a platform purpose-built to support GPU-intensive workloads with enhanced performance, automation, and security.

In this guide, we’ll explore how to optimize GPU workloads using vSphere 9.0, from hardware configuration to workload placement and lifecycle management.

Why vSphere 9.0 for GPU Workloads?

vSphere 9.0 introduces several enhancements that make it ideal for AI/ML, deep learning, and high-performance computing (HPC) environments:

• Faster vMotion for GPU-powered VMs Seamlessly migrate GPU-enabled virtual machines with minimal downtime and latency.

• Improved DRS (Distributed Resource Scheduler) Smarter workload placement ensures optimal GPU utilization across clusters.

• Support for NVIDIA vGPU and MIG (Multi-Instance GPU) Run multiple isolated workloads on a single GPU, maximizing ROI.

• Advanced Memory Tiering with NVMe Reduce DRAM dependency and improve memory throughput for data-intensive tasks.

Best Practices for Running GPU Workloads on vSphere 9.0

1. Choose the Right Hardware

Ensure your ESXi hosts are equipped with NVIDIA A100, H100, or L40 GPUs, and that they’re certified on the VMware Compatibility Guide (VCG). Use PCIe Gen4 or NVLink for high-bandwidth interconnects.

2. Enable NVIDIA vGPU and Install Guest Drivers

Use NVIDIA vGPU Manager on the ESXi host and install the corresponding guest OS drivers. This enables vGPU profiles that match your workload needs—whether it’s training large models or running inference at the edge.

3. Leverage vSphere DRS and Affinity Rules

Use DRS with GPU-aware placement policies to avoid contention and ensure workloads are placed on hosts with available GPU resources. Set VM-Host affinity rules for latency-sensitive applications.

4. Monitor GPU Utilization with vRealize Operations

Integrate vRealize Operations or Aria Operations to track GPU usage, temperature, and memory consumption. Set alerts for underutilized or overcommitted GPUs.

5. Use vSphere Lifecycle Manager (vLCM)

Automate firmware, driver, and ESXi updates across GPU-enabled clusters using vLCM. This ensures consistency and reduces downtime during maintenance.

Integrating with Kubernetes and Tanzu

Running GPU workloads in Kubernetes? vSphere 9.0 with Tanzu Kubernetes Grid (TKG) supports GPU passthrough and vGPU sharing in containerized environments. This is ideal for AI pipelines, computer vision, and real-time analytics.

Security and Compliance

vSphere 9.0 is secure by default, with TLS 1.3, FIPS 140-2 compliance, and vTPM support. These features are critical when running sensitive AI workloads in regulated industries like healthcare and finance.

Use Cases

• AI/ML Model Training and Inference
• Video Rendering and 3D Simulation
• Genomics and Bioinformatics
• Financial Risk Modeling
• Autonomous Vehicle Simulation