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What could Paralleliq save your fleet?

Estimate the annual value across margin recovery, incident prevention, and operational efficiency — based on your fleet size and current spend.

Your Fleet

For hosted model API providers, inference deployment platforms, and enterprise AI teams.

GPU Count64 GPUs

8 GPUs2048 GPUs

All-in cost per GPU ($/hr)$3.5/hr

$1/hr$15/hr

Estimated GPU waste50%

Paralleliq estimates about half. Most teams don't know their real number until they scan.

5%60%

GPU ops & incident response (hrs/week)10 hrs/wk

OOM triage, tier changes, customer escalations, utilization reviews.

1 hrs/wk40 hrs/wk

Annual GPU spend

$2.0M

Of which $981K is estimated waste: tier misplacement, idle capacity, and throughput suppression.

Waste recovered

$196K – $392K

per year, at our 20–40% target

Ops hours freed

364

incidents, triage, escalations

Ops cost saved

$55K

per year

Total annual value

$251K – $447K

3-year value

$752K – $1.3M

Assumes Paralleliq's target of recovering 20% to 40% of your estimated waste, 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.

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.

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