What is a critical consideration for virtualizing GPU-accelerated infrastructure for AI workloads?

Ensuring proper NUMA (Non-Uniform Memory Access) alignment is crucial when virtualizing GPU-accelerated infrastructure for AI workloads because it directly impacts the performance and efficiency of the system. NUMA architecture is designed to improve memory access times for CPUs by organizing multiple CPUs and memory into nodes. Each CPU or core has its own local memory, and accessing memory within the same node is significantly faster than accessing memory from another node.

In an AI workload that typically demands high data throughput and low latency, improper NUMA alignment can lead to bottlenecks where virtual machines (VMs) may be accessing memory across nodes, resulting in increased latency and degraded performance. Proper alignment involves configuring the system such that VMs utilizing the GPU also have close access to their required memory resources, which minimizes cross-node traffic and enhances performance for AI computations.

The other options do not address this critical alignment aspect as effectively. For instance, while software-based GPU virtualization may have its benefits, it does not inherently solve performance issues related to memory access patterns. Similarly, maximizing the number of VMs per GPU or allocating more vCPUs than physical CPUs can create contention and diminish the overall efficiency of the infrastructure. Therefore, ensuring proper NUMA alignment stands out as a paramount consideration