GPU Optimisation and Utilisation Audits
We find idle and wasted GPU capacity, then fix the scheduling, sharing and policies that cause it, so more work runs on the hardware you already own.
Most GPUs sit mostly idle
Pay for use, not for idle time
Cast AI's 2026 report on about 23,000 enterprise Kubernetes clusters found average GPU utilisation of around 5 percent. Much of the cost is idle capacity.
We look at how your GPUs are scheduled and shared, and recommend changes that raise use without hurting the teams that depend on them.
Who this is for
Organisations running their own GPU servers or clusters for training or inference, and finance teams asking where the spend goes.
How we work
We start by measuring: which jobs use which GPUs, how long they wait, and how much capacity is idle.
We then propose changes: better scheduling, sharing a GPU between smaller jobs, and policies that keep teams within agreed limits.
We can also set up the monitoring and policy controls so the gains last.
What you get
Utilisation and cost report for your GPUs.
List of idle and wasted capacity.
Recommended scheduling and sharing changes.
Policy and usage-limit design.
Monitoring dashboard setup.
Follow-up review after changes.
