The Uptime Institute highlights the high costs of GPU accelerators used in AI, such as Nvidia H100, which can range from $27,000 to $40,000. While renting one on cloud platforms like Azure is an alternative, inefficiencies persist. The report discusses how AI teams commonly overestimate their GPU utilization, believing it's higher than it actually is. Approximately 80% of GPU servers are operational time-wise, but only 35-45% of their computational capabilities are utilized. A better metric for measuring GPU usage is necessary to improve performance and reduce waste in the industry.
GPU accelerators used in AI processing are costly items, making their efficient usage a priority, yet the industry lacks effective measurement methods, says the Uptime Institute.
Users want to keep their GPU units working efficiently, yet current tracking methods are often inadequate and do not reflect true performance utilization.
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