When a team notices a 60% spike in inference latency, dashboards still report GPU utilization at 79‑84%. Autoscaling adds nodes, inflating cloud costs, while latency barely drops. The culprit turns out to be three machines silently entering degraded RAID rebuild states, throttling storage throughput and starving nearby workloads.

Modern GenAI clusters hide similar failures behind healthy metrics. A node may keep GPUs and memory online while its disk rebuild drags SSD bandwidth, leaving the scheduler to misjudge availability. The result is resource fragmentation: spare GPUs sit on nodes whose I/O or storage paths are saturated, so new jobs cannot fit cleanly.

The economic hit is stark. A 1,000‑GPU H100 fleet running at about $3/GPU‑hour costs roughly $26 million per year. If fragmentation wastes just 10% of productive GPU time, the loss climbs to about $2.6 million annually—money that could fund better hardware or software optimization for research and scaling.

Residual‑Aware Geometric Packing (RAGP) shifts scheduling focus from isolated GPU counts to multidimensional resource shapes. By simulating post‑placement leftovers, RAGP favors nodes that leave useful capacity for future jobs, breaking the cycle of fragmentation and wasted GPU hours. Infrastructure teams must adopt such policies to align cost with real throughput.