When setting up a virtualized environment with NVIDIA GPUs, you notice a significant drop in performance compared to running workloads on bare metal…

Overcommitting GPU resources is the most likely cause of performance degradation in a

virtualizedenvironment with NVIDIA GPUs. In virtualization setups using NVIDIA vGPU technology,

overcommitting occurs when more virtual machines (VMs) request GPU resources than are physically

available, leading to contention and reduced performance compared to bare metal. NVIDIA’s vGPU

documentation warns that proper resource allocation is critical to avoid this issue, as GPUs are not as

easily time-sliced as CPUs. Option A (high-performance networking) typically enhances, not

degrades, performance. Option C (SSD storage) improves I/O but doesn’t directly impact GPU

performance. Option D (high availability) adds redundancy, not significant GPU overhead. NVIDIA’s

guidelines emphasize avoiding overcommitment for optimal virtualized AI workloads.

Reference: NVIDIA vGPU Technology (www.nvidia.com), AI Infrastructure for Enterprise

(www.nvidia.com).