AI workloads are shifting from primarily model training to serving these models through inferencing, making network performance and stability critically important. GPUs are data hungry, demanding continuous, low-latency data streams. It’s paramount to optimize the network infrastructure before receiving expensive GPUs, ensuring readiness for new generations of hardware and complex workloads. The goal is to avoid treating users as “beta testers” by proactively validating system performance and limits.

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