The hidden backbone of AI: Why the network decides your GPU ROI
By Shekar Ayyar, CEO and Chairman, Arrcus
As AI becomes foundational to companies, it’s time to ask some critical questions. Is your AI really scaling intelligence, or inefficiency? The infrastructure powering AI is not just expensive; it is often profoundly inefficient. A single modern AI GPU can consume up to 37 megawatt-hours of electricity each year. That’s enough to power multiple homes. Yet even in large, state-of-the-art AI clusters, real-world utilization frequently hovers between just 50% and 70%, leaving costly capacity underused. In multimodal workloads, the inefficiency is even more striking, with as much as 84% of GPU power going to waste.
If your organisation has set aside a significant budget for building AI infrastructure, you may want to take note. A growing share of your most expensive assets might be sitting idle, consuming huge amounts of power, generating heat, and delivering far less value than expected.
The uncomfortable truth...
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