GPU Waste Calculator.
Paralleliq estimates that about half of GPU capacity is wasted: idle capacity, fragmentation, and GPUs starved of data. Enter your monthly spend to see your estimate and the 20–40% of that waste Paralleliq targets to recover.
- 1. Over-provisioning & unattended idling
Capacity sized for peak demand and left running when traffic drops.
- 2. Whole-GPU fragmentation
Free GPUs scattered across nodes in pieces too small for the next job.
- 3. CPU-to-GPU imbalance & data starvation
GPUs waiting on preprocessing and data loading that can't keep up.
Paralleliq estimates that about half of GPU capacity is wasted across causes like these. That figure is our estimate, not a published one, and actual waste varies by cluster.
These three are common, not the full list. Others include models running on the wrong GPU tier, serving engines configured below what the hardware can do, and nodes that are allocated but serve no traffic. A scan shows which ones apply to your cluster.
Published research consistently reports GPU utilization below 50%. It measures how much capacity goes unused, which is the context for our estimate; it does not say how much is wasted.
- “production AI workloads often achieve well below 50% sustained utilization, even under load” Anyscale, 2026
- “average GPU utilization in the enterprise is stuck at 5%” VentureBeat, 2026 (reporting Cast AI data)
Want a real number?
Paralleliq Scanner (piqc) scans your Kubernetes cluster in seconds. No agents, no instrumentation, nothing changes in your cluster.