What could Paralleliq save your fleet?
Estimate the annual value across waste recovery, operational efficiency, and engineering hours freed — based on your fleet size and current spend.
For GPU cloud providers, inference platforms, and enterprises running on-prem clusters.
Industry average: 30–40%. Most teams don't know their real number until they scan.
Incident triage, tier changes, utilization reviews, tenant troubleshooting.
Assumes 65% of estimated waste recovered and 70% of manual ops hours automated. Based on 64 GPUs at $3.5/hr and an ML infra engineer cost of $150/hr. Actual results depend on your workload mix and current tooling.
Want the full breakdown for your actual cluster — not estimates?
Ready to see your real number?
These are estimates. A 30-minute call with a piqc scan of your cluster shows you the actual waste, misplacement, and dark capacity — before you spend anything.
More Calculators
View all →GPU Waste Calculator
Estimate how much your inference fleet could recover through rightsizing.
GPU Inference TCO Calculator
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Build vs. Buy: GPU Control Plane
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GPU Sizing Calculator
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Inference Capacity Planner
Plan GPU capacity based on your model, traffic, and latency targets.
GPU Fleet Cost Optimizer
Find the lowest-cost configuration for your throughput requirements.
KV Cache & Context Window Cost
See how KV cache memory scales with context length and batch size.
CPU:GPU Ratio Calculator
Find the gap as AI shifts from batch inference to multi-agent orchestration.