The AI efficiency gap: from TCO crisis to optimized cost and performance

These new workloads are architecturally different from legacy applications. Training large models requires massive clusters of specialized accelerators, such as GPUs and TPUs, which must work in parallel and communicate constantly. This, in turn, demands an extremely high-bandwidth, low-latency “fabric” that is far more robust than standard cloud Ethernet because the primary bottleneck is often the network itself.

Additionally, petabyte-scale data sets feed these models, requiring a high-performance storage system that can stream data at incredible speeds to prevent the expensive accelerators from sitting idle. This infrastructure must also serve two distinct purposes: the long-running, monolithic jobs of AI training and the short, real-time, bursty requests of AI inference.

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The AI efficiency gap: from TCO crisis to optimized cost and performance