In an AI infrastructure architecture, what approach can alleviate the burden on CPUs during intensive workloads?

Implementing specialized processors for certain tasks is a highly effective approach to alleviate the burden on CPUs during intensive workloads in AI infrastructure. This is primarily because specialized processors, such as GPUs (Graphics Processing Units) or TPUs (Tensor Processing Units), are designed to handle specific types of computations more efficiently than general-purpose CPUs.

In AI tasks, particularly in training machine learning models, the computational demands can be significant. Specialized processors can perform many operations in parallel, making them particularly suited for matrix calculations and other operations commonplace in AI workloads. This offloading of tasks from the CPU not only enhances overall system performance but also enables the CPU to focus on other tasks, optimizing resource utilization.

Other options may provide some benefits, but they do not directly address the efficiency of computational tasks in the same manner. For instance, load balancing across servers can improve overall performance and prevent individual server overload, but it does not specifically reduce the CPU's workload during intensive tasks. Similarly, using high-priority training jobs can help in managing job execution order but does not inherently decrease CPU demand or improve computational efficiency. Reducing the number of GPUs in use would likely lead to greater strain on CPUs since the overall computational workload would not see a significant reduction, making this strategy counterproductive for