Column | Is low GPU utilization really waste? The trap FinOps misses in security AI training

▤ Summary

This column argues that low GPU utilization in AI work is not automatically waste. It explains that many security AI training workloads are memory-bound, so standard FinOps rightsizing metrics can miss the real compute profile. The article uses examples such as differential privacy and adversarial training to show how AI teams can overfocus on GPU occupancy instead of model accuracy and robustness. It urges CIOs and IT leaders to evaluate ML workloads by compute behavior, data characteristics, and chargeback patterns before making optimization decisions.

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