An administrator is troubleshooting a bottleneck in a deep learning run time and needs consistent...

Comprehensive and Detailed Explanation From Exact Extract:

When troubleshooting performance bottlenecks related to feeding data consistently to GPUs during deep learning workloads, the key storage metric to consider is sequential read speed. Deep learning training typically involves streaming large datasets sequentially from storage to GPUs. The sequential read speed measures how fast data can be read in a continuous stream, directly impacting the ability to keep GPUs fed without stalls.

Disk I/O operations per second (IOPS) measures random read/write operations and is less relevant for large sequential data streams in AI workloads.

Disk free space indicates available storage capacity but does not impact data feed rate.

Disk utilization in performance manager shows overall usage but does not specify the speed or consistency of data feed.

Therefore, focusing on sequential read speed (option C) is critical for ensuring consistent, high-throughput data feeding to GPUs, minimizing bottlenecks in deep learning runtime environments.

This is consistent with NVIDIA AI Operations best practices for system performance optimization and troubleshooting storage-related issues in AI infrastructure.

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