Free Tool
GPU Fleet Cost Optimizer.
Model your mixed GPU fleet — A100s, H100s, T4s — and see which groups are over-tiered, under-utilized, or carrying recoverable waste. Industry average: 30–50% idle.
GPU Type
Count
Util %
Workload / Model
Recoverable Annual Spend
$159,414
33% of $484,954/yr fleet cost
$484,954
Current annual
$325,539
Optimized annual
33%
Waste rate
40
Total GPUs
Per-group analysis
| Group | Annual | Waste | Status |
|---|---|---|---|
8× NVIDIA A100 80GB 70B+ · 22% util Consider reducing to ~6 GPUs | $287,328 | $86,198 | MEDIUM |
20× NVIDIA L4 (24GB) Mixed workloads · 38% util Consider reducing to ~15 GPUs | $141,912 | $42,574 | MEDIUM |
12× NVIDIA T4 (16GB) 7B – 13B · 18% util Scale down to ~4 GPUs | $55,714 | $30,642 | HIGH |
Significant idle capacity across your fleet. 33% of your annual GPU spend — $159,414 — is going to waste. The biggest lever is consolidating underutilized nodes before adding capacity.
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Waste is estimated from utilization rate and GPU-to-model tier fit. Industry average GPU utilization in AI inference is 25–35%.
Ready for actual fleet telemetry?
These estimates are based on your inputs. ParallelIQ Introspect reads live GPU metrics from your Kubernetes cluster and shows real utilization, misplacement, and dark capacity — across every node, every pod.