Dedicated GPU infrastructure can beat the public cloud on cost. Companies considering purchasing an AI cluster need to consider utilization as the key variable in their calculations.
Supersized generative AI models are placing onerous demands on both IT and facilities infrastructure. The challenge for next-generation AI infrastructure will be power, forcing operators to explore new electrification architectures.
Nvidia’s dominant position in the AI hardware market may be steering data center design in the wrong direction. This dominance will be harder to sustain as enterprises begin to understand AI and opt for cheaper, simpler hardware.
Visibility into costs remains a top priority for enterprises that are consuming cloud services. Improving the tagging of workloads and resources may help them to spot, and curb, rising costs.
Not all generative AI applications will require large and dense infrastructure footprints. This complicates AI power consumption projections and data center planning.
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.
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.
Uptime Institute's 2024 IT and Power Efficiency Survey (n=941) focuses on strategies for managing data center IT equipment inventories, gathering data for accuracy and completeness, and the collection of storage and server information.The attached…
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 — and may be difficult to manage at scale.
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.
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?
Several recent outages have exposed the global dependency on a small number of third-party suppliers — and governments around the world are already taking note.
Metered-by-outlet iPDUs present a relatively straightforward method of collecting server-level power consumption data. This information will be increasingly important to data center efficiency — making iPDUs a more popular choice.
Dr. Owen Rogers
Daniel Bizo
Max Smolaks
Anthony Sbarra
Laurie Williams
Dr. Tomas Rahkonen
John O'Brien
Andy Lawrence