Not all generative AI applications will require large and dense infrastructure footprints. This complicates AI power consumption projections and data center planning.
Enterprises have much enthusiasm for AI, interviews and workshops by Uptime Intelligence suggest, but this is tempered by caution. Most hope to avoid disruptive, expensive or careless investments.
Many organizations still do not tap into the potential power efficiency gains hidden in servers. Without operational focus on extracting those, future server platforms may bring marginal, if any, energy performance improvements.
Enterprises have various options on how and where to deploy their AI training and inference workloads. This report explains how these different options balance cost, complexity and customization.
To meet the demand driven by AI workloads, a new breed of cloud provider has emerged, delivering inexpensive GPU infrastructure as a service. Their services are highly demanded today, but longer-term, the market is ripe for consolidation.
Powerful solar storms have already brought warnings of disruption to electricity grids and their customers twice in 2024 β and the Sunβs activity has yet to peak. Why do data centers and power utilities appear to have escaped unscathed?
While GPUs are the power-hungry devices that enable effective AI training, it is innovations in software that are fueling the recent surge in interest and investment. This report explains how neural networks power generative AI.
Although quantum computing promises a revolution in scientific discovery, its use is still constrained to research and continuing development. However, a new IBM quantum data center in Germany signals a growing interest in its capabilities.
AI training clusters can show rapid and large swings in power consumption. This behavior is likely driven by a combination of properties of both modern compute silicon and AI training software β and may be difficult to manage at scale.
Uptime analysis suggests a growing interest in public cloud by financial institutions. But concerns over cloud providersβ support for regulation compliance ahead of the EUβs Digital Operational Resilience Act may cause some to pull back.
Increasing supply air temperature is gaining interest as an approach to potentially save data center energy. However, savings will not be universally possible and understanding its potential involves a complex multivariable analysis.
Pulling reliable power consumption data from IT is increasingly important for operators. Although third-party software products offer promise, significant roadblocks still hinder adoption.
Densification is β once again β high on the agenda, with runaway expectations largely due to compute power requirements of generative AI workloads. Will this time be different? Uptimeβs 2024 global survey of data center managers offers some clues.
Software updates by third-party IT providers occur every day, either in the cloud or on-premises. The recent global IT outage has exposed a hard truth: that another major event is likely to occur.
Most operators will be familiar with the outrageous power and cooling demands of hardware for generative AI. Why are these systems so difficult to accommodate, and what does this mean for the future of data center design?