A new analysis argues that average GPU utilization can give AI teams a false sense of infrastructure efficiency.
The problem is that a single utilization number can hide stalls, imbalance, and scheduling gaps that slow actual training or inference throughput. A cluster can look busy while still wasting expensive compute.
That matters as AI infrastructure budgets rise: teams need better systems observability if they want to improve performance instead of just buying more GPUs.