We spent about two weeks fixing our GPU metrics by enabling the normally disabled DCGM profiling counters and correcting which workload each measurement was attributed to. The effort paid off: across two Kubernetes clusters, we found eleven GPUs below 5% SM activity for an entire month. One was holding 40 GB of framebuffer while serving nothing.
The utilization data was accepted as accurate during capacity review, but every GPU had a justification. Two are being held as launch headroom, three were acquired by a team during the shortage and they refuse to release them, and another belongs to a research system with a stale CUDA context left behind by someone who left months ago. Nobody on that team wants to take responsibility for shutting it down.
The report identifies the cost, but it does not create accountability. An intentionally reserved GPU and an accidentally abandoned GPU look identical in utilization data, and neither reservation reason is recorded anywhere reliable. Labels seem like the obvious solution, but they rarely stay accurate after the first month.
How are you distinguishing deliberate capacity reservations from forgotten or abandoned allocations? Do you use internal billing, ownership, expiring reservations, or some other process that teams actually maintain?
3 Answers
Treat each reservation as a lease rather than a permanent label. Give it an owner, a business reason, an expiry date, and a renewal process. When the lease expires, the GPU should either be released or renewed with an explicit justification. That turns idle capacity into an accountable decision instead of an undocumented exception.
Internal chargeback can make this much easier. Tag the GPU nodes and allocate their cost to the owning team, including reserved capacity that is not actively being used. If a group still wants to pay for idle headroom, that is a valid business choice; the important part is making the cost visible to the people making the reservation.
The utilization report is doing its job, but the organization needs a policy layered on top of it. Define who owns every GPU, require reservations to have an expiry and an expected start date, and automatically flag stale CUDA contexts or unclaimed workloads for review. Eleven cards may be small financially, but permanent undocumented exceptions are how much larger capacity problems start.
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