Study: Calculating the Total Cost of a GPU Cluster
Learn how to evaluate the true total cost of AI infrastructure beyond GPU pricing and identify the hidden factors that impact ROI at scale.
Most organizations evaluate AI infrastructure based on GPU hourly pricing. However, GPU costs alone rarely reflect the true economics of training and inference at scale.
This report, developed by Nebius and SemiAnalysis, examines the full total cost of ownership (TCO) of GPU clusters and reveals how factors such as networking, storage, utilization, operational overhead, cluster performance, and reliability can significantly impact overall infrastructure costs.
Using real-world deployment scenarios and pricing data, the report compares leading cloud providers and demonstrates how hidden cost drivers can materially affect the economics of AI workloads even when GPU hourly rates appear identical.
Download the report to learn:
- How infrastructure reliability and cluster design shape the true cost per completed model
- How downtime, recovery time, and wasted compute quietly compound at scale
- Why networking performance and operational maturity determine real-world cluster productivity
- Why Nebius can deliver lower overall TCO, even when headline GPU pricing appears comparable
Get the report to better understand the economics of AI infrastructure and make more informed decisions about your GPU strategy.