Data center executives, infrastructure architects, and AI platform leaders responsible for optimizing GPU utilization, managing AI infrastructure costs, and scaling inference workloads to exabyte capacity.
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Storage bottlenecks are the primary cause of GPU underutilization — inadequate storage performance can effectively double cost per token in under-utilized clusters by starving GPUs of data.
Legacy architectures break down beyond ~100 PB with namespace fragmentation and performance cliffs, while ExaPOD's exascale-native design delivers linear capacity and performance scaling to 1 EiB and beyond.
Power, not space, is the hard constraint in AI infrastructure — storage operating at ~900 W/PiB can unlock the equivalent of 450 additional GPU servers within an existing facility's power envelope.
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