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.
Max Smolaks
Max is a Research Analyst at Uptime Institute Intelligence. Mr Smolaks’ expertise spans digital infrastructure management software, power and cooling equipment, and regulations and standards. He has 10 years’ experience as a technology journalist, reporting on innovation in IT and data center infrastructure.
msmolaks@uptimeinstitute.com
Latest Research
Nvidia’s vision for data centers may be a false prophecy
REPLAY | Uptime Intelligence Client Webinar: Cooling the AI heat wave
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In this inaugural Uptime Intelligence client webinar, Uptime experts discuss and answer questions on cooling technologies and strategies to address AI workloads. Uptime Intelligence client webinars are only available for Uptime Intelligence…
Why bigger is not better: gen AI models are shrinking
Not all generative AI applications will require large and dense infrastructure footprints. This complicates AI power consumption projections and data center planning.
Should data centers sell their heat?
Historically, data center waste heat recovery has been promoted with a focus on the benefits for the heat off-taker. And yet, the overall winner in most situations is the data center operator — even if they are not paid for heat.
AI has a weight problem
Generative AI models brought about an influx of high-density cabinets. There has been much focus on how to best manage thermal issues, but the weight of power distribution equipment is a potentially overlooked concern.
Hardware for AI: what makes it different
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?
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Max
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The 14th edition of the Uptime Institute Global Data Center Survey highlights the experiences and strategies of data center owners and operators in the areas of resiliency, sustainability, efficiency, staffing, cloud and AI.
Global IT disruption highlights concentration, third-party risk
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.
iPDUs: a critical step for next generation efficiency
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.
The impact of AI on data center operations (Part I)
This report outlines the characteristics of machine learning (ML) applications, describes production use cases for ML-based software in data center M&O, and profiles several vendors offering AI-based functionality in their products.
UK prepares resiliency, cybersecurity legislation for colocation
Operators of data centers in the UK have voiced concerns over the proposed resiliency and cybersecurity regulations that are aimed specifically at providers of colocation and “co-hosting” services.
What is the role of AI in digital infrastructure management?
This update examines the differences between machine learning and traditional software development and outlines the terms and definitions that may help digital infrastructure operators to understand the role and impact of AI.
REPLAY | Webinar: Five Data Center Predictions for 2024
In this Webinar, the Uptime Intelligence team looks beyond the obvious trends of 2024 and identifies some of the latest developments and their associated limitations.
Five data center predictions for 2024
Uptime Intelligence looks beyond the more obvious trends of 2024 and identifies some challenging issues. Strong IT demand, high-density IT systems and tough sustainability requirements will drive a new wave of investment.
Data center management software is evolving — at last
A new approach to data center management, proposed by data scientists and statisticians, looks to augment the functionality of tools like BMS and DCIM software by focusing on data, not equipment.
