In a virtualized AI infrastructure, what are two critical factors to ensure GPU-accelerated applications run smoothly?

Ensuring the hypervisor supports GPU virtualization is essential for the effective performance of GPU-accelerated applications in a virtualized AI infrastructure. GPU virtualization allows multiple virtual machines (VMs) to share the resources of a physical GPU, facilitating efficient utilization and management of these powerful processing units. If the hypervisor does not support GPU virtualization, it cannot allocate GPU resources dynamically among various VMs, which can lead to underperformance or the inability to effectively run GPU-intensive applications.

In a virtualized environment, the ability to leverage GPU virtualization helps ensure that applications that rely on GPU acceleration can perform optimally, providing the necessary computing power and resources they require. This capability also supports scalability, enabling infrastructure to adapt as workloads increase.

While other factors like network security, storage IOPS, and CPU configurations may contribute to overall system performance, they do not directly address the requirements of utilizing GPU resources efficiently within a virtualized environment. Thus, the support for GPU virtualization by the hypervisor is a critical factor that directly impacts the smooth operation of GPU-accelerated applications.