Written by

Anna Rasumussen

Marketing Director

Mismanaging GPU resources can lead to skyrocketing costs and stalled projects. This article explores common pitfalls and how teams can avoid them.

AI projects live and die by GPU performance. Yet many teams underestimate how quickly costs can spiral when resources aren’t managed correctly.

Common pitfalls include:

  • Idle GPU sessions: Leaving compute power running when not in use.

  • Over-provisioning: Choosing high-end GPUs for tasks that don’t need them.

  • Lack of monitoring: Not tracking usage in real time, leading to surprise billing.

How to avoid these issues:

  • Right-size workloads. Match GPU type to the task at hand.

  • Automate shutdowns. Ensure sessions end when not in active use.

  • Track in real time. Use dashboards and alerts to stay on top of usage.

By approaching GPU usage with intention, companies can unlock high-performance AI without draining their budgets.

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