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Nearly half of enterprises waste millions on underused GPUs despite cost concerns, prompting tools like ClearML to boost efficiency via fractional GPU sharing.
A new ClearML report reveals that nearly half of enterprises are wasting millions due to underutilized GPU capacity despite prioritizing cost control and efficiency in 2025–2026.
While 35% aim to improve GPU utilization, 44% still rely on manual workload assignment or lack formal strategies, creating delays in AI development.
Cost management is the top challenge for 53%, and governance of data, models, and compute is a key priority for many.
To address inefficiencies, ClearML has expanded support for fractional GPU partitioning on AMD Instinct GPUs, enabling multiple workloads to run simultaneously on a single GPU with automated, centralized management.
The silicon-agnostic platform improves resource efficiency, reduces idle capacity, and supports heterogeneous environments—helping enterprises maximize ROI without increasing infrastructure costs.
Casi la mitad de las empresas desperdician millones en GPUs subutilizadas a pesar de las preocupaciones de costos, lo que lleva a herramientas como ClearML a aumentar la eficiencia a través del uso compartido de GPU fraccionado.