What critical factor must be considered when virtualizing an infrastructure that includes GPUs for AI workloads?

When virtualizing an infrastructure that includes GPUs for AI workloads, a critical factor to consider is the use of GPU sharing technologies like NVIDIA GRID. This is because AI workloads often demand significant computational resources that can exceed what a single GPU can provide when operating dedicatedly for a single virtual machine.

NVIDIA GRID allows multiple virtual machines to share the resources of a single physical GPU. This capability is particularly beneficial for AI workloads, which are typically resource-intensive. By enabling GPU sharing, organizations can maximize their hardware investments, improve resource utilization, and ensure that multiple AI models can be trained or run in parallel without needing to dedicate a full GPU to each task. This results in better performance efficiency and cost-effectiveness.

The other options, while they may have relevance in different contexts, do not address the specific needs of GPU virtualization for AI workloads. For instance, simply increasing the number of virtual CPUs or assigning more storage to each virtual machine does not optimize GPU usage and might lead to inefficiencies. Disabling hyper-threading could potentially underutilize CPU resources and does not directly relate to effective GPU utilization. Therefore, employing technologies like NVIDIA GRID stands out as the most critical consideration when optimizing GPU resources in a virtualized AI infrastructure.