What is a good GPU utilization target?

Most fleets run at 20–40% average utilization. Raising to 60–70% via right-sizing, GPU sharing (time-slicing/MIG), idle reclamation, and autoscaling typically cuts the GPU bill by 30–60% for the same useful work.

GPU Idle-Cost Calculator

Most Kubernetes GPU fleets waste 60–70% of their spend on allocated-but-idle cards. Enter yours to see the annual number — and what you can reclaim.

Number of GPUs

GPU type (list $/hr — edit to yours)

Allocated hours/day (24 = always-on)

Average GPU utilization: 30%

Target utilization after optimizing: 70%

Monthly GPU spend

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Wasted on idle / month

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Wasted on idle / year

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Effective $/useful GPU-hr

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List prices are editable — plug in your real negotiated / cloud rate. Math & assumptions below.

Seeing the waste is step one. Reclaiming it is step two.

These copy-paste Kubernetes packs turn this number down — measure per-GPU utilization, then share/partition/reclaim idle GPUs:

GPU Monitoring Pack — see the idle 70% → GPU Cost-Optimization Pack — reclaim it →

Why idle GPUs are the biggest line item nobody watches

A GPU costs the same whether it's running at 100% or sitting at 0% — you pay for the allocation, not the work. So utilization is a direct multiplier on your real cost. Run a fleet at 30% average utilization and you're paying 3.3× per useful GPU-hour versus a fleet at 100%. Across a rack of H100s that's tens of thousands of dollars a month evaporating on cards that are allocated, powered, billed — and idle.

How the math works

Value	Formula
Monthly spend	GPUs × $/hr × hours/day × 30.4
Wasted on idle	Monthly spend × (1 − utilization)
Effective $/useful hr	$/hr ÷ utilization
Reclaimable savings	Annual spend × (1 − current ÷ target utilization)

Reclaimable savings model the fact that doing the same useful work at a higher utilization needs proportionally fewer GPUs. It's a planning estimate, not a guarantee — real savings depend on workload shape.

How teams actually cut it

Measure per-GPU utilization — DCGM → Prometheus → Grafana, per card, per pod. You can't cut what you can't see.
Share GPUs for dev/notebooks/light inference (time-slicing) or partition them (MIG) so small models stop eating whole cards.
Cap hoarding with per-namespace GPU quotas.
Run interruptible work on spot nodes (60–90% cheaper).
Reclaim idle allocated GPUs automatically instead of hoping someone notices.

FAQ

How much do idle GPUs cost?
An idle H100 (~$2.50/hr) burns ~$1,800/month at 0% utilization. At 30% fleet utilization, ~70% of your GPU bill is paying for idle time.

What's a good GPU utilization target?
Most fleets sit at 20–40%. Getting to 60–70% typically cuts the bill 30–60% for the same work.

How do I reduce Kubernetes GPU costs?
Measure utilization, share/partition GPUs, quota them, use spot for interruptible jobs, and auto-reclaim idle allocations.