In brief: GPU clusters in enterprises run at average utilisation below 25 percent because data storage and network cannot supply the fast hardware adequately.
Many enterprises operate expensive GPU clusters with utilisation rates below 25 percent, while operating costs per instance reach five-figure annual amounts. The reason often lies in poorly prepared data streams that block the hardware.
Enterprise organisations have invested massively in GPU capacity in recent years or concluded long-term cloud contracts for high-end hardware such as Nvidia’s Hopper and Blackwell architectures. This was based on the strategic assumption that the availability of high-performance compute automatically leads to faster AI value creation. This calculation has worked out — in theory.
In operational reality, empirical surveys by cloud FinOps researchers and infrastructure service providers such as Granulate reveal a stark disparity: while classical CPU servers, optimised through decades of virtualisation, are often utilised at over 70 percent, dedicated enterprise GPUs in many industries fall below 25 percent utilisation. Since a single modern server GPU incurs annual costs in the five-figure range, losses due to idle time in larger clusters add up to millions of euros. CFOs are therefore increasingly demanding transparent cost-per-inference models and justification of tied-up capital.
The main cause lies in data provisioning (data starvation): modern accelerators process matrix operations faster than legacy data storage or standard cloud object storage can provide this data over the network. The I/O bottleneck is typical — while GPU compute cores process data in milliseconds, the hardware waits many seconds for the next data packet from fragmented storage systems. High-frequency data provisioning with minimal latency would be required, but is not planned for in many architectures.
CTOs must therefore reassess pipeline optimisation, data procurement and infrastructure design. Mere compute capacity without predefined data flows and network dimensioning becomes a pure balance sheet burden.
Source: www.it-daily.net · Published 2 August 2026
Lumi AI News — AI-assisted curation pursuant to Article 50 EU AI Act. Paraphrase and classification by Lumi News Pipeline v1.7.3.