What can most likely resolve the issue of underutilized GPUs and high CPU usage in an AI data center?

Optimizing the data pipeline for better I/O throughput is a strategic approach to addressing the issue of underutilized GPUs and high CPU usage in an AI data center. When the data pipeline is efficient, it ensures a smooth flow of data from storage to the GPUs, minimizing bottlenecks and waiting times. High CPU usage often indicates that the CPUs are spending too much time preparing data for the GPUs rather than letting the GPUs do the heavy lifting of computations. By optimizing the data pipeline, you can improve how data is fetched, processed, and transferred to the GPUs, allowing them to run at their full potential and thus reducing CPU load.

In contrast, reducing the batch size during training could result in more frequent GPU utilization but may not necessarily resolve the issue of high CPU usage if the data supply to the GPUs remains inefficient. Increasing GPU memory allocation might help in scenarios with memory constraints, but it does not directly address the imbalance between CPU and GPU utilization. Adding more GPUs could spread out the workload, but without optimizing the data pipeline, the new GPUs may still be underutilized due to the same data delivery issues.