Digital twins are increasingly valued in complex data center applications, such as designing and managing facilities for AI infrastructure. Digitally testing and simulating scenarios can reduce risk and cost, but many challenges remain.
While AI infrastructure build-out may focus on performance today, over time data center operators will need to address efficiency and sustainability concerns.
AI vendors claim that “reasoning” can improve the accuracy and quality of the responses generated by LLMs, but this comes at a high cost. What does this mean for digital infrastructure?
Data center builders who need power must navigate changing rules, unpredictable demands — and be prepared to trade.
Quantum computing progress is slow; press releases often fail to convey the work required to make practical quantum computers a reality. Data center operators do not need to worry about quantum computing right now.
Chinese large language model DeepSeek has shown that state of the art generative AI capability may be possible at a fraction of the cost previously thought.
AI is not a uniform workload — the infrastructure requirements for a particular model depend on a multitude of factors. Systems and silicon designers envision at least three approaches to developing and delivering AI.
SMRs promise to usher in an era of dispatchable low-carbon energy. At present, however, their future is a blurry expanse of possibilities rather than a clear path ahead, as key questions of costs, timelines and operations remain.
Rapidly increasing electricity demand requires new generation capacity to power new data centers. What are some of the new, innovative power generation technology and procurement options being developed to meet capacity growth and what are their pote...
Agentic AI offers enormous potential to the data center industry over the next decade. But are the benefits worth the inevitable risks?
The cost of low-carbon green hydrogen will be prohibitive for primary power for many years. Some operators may adopt high-carbon (polluting) gray hydrogen ahead of transitioning to green hydrogen
Dedicated AI infrastructure helps ensure data is controlled, compliant and secure, while models remain accurate and differentiated. However, this reassurance comes at a cost that may not be justified compared with cheaper options.
AI infrastructure increases rack power, requiring operators to upgrade IT cooling. While some (typically with rack power up to 50 kW) rely on close-coupled air cooling, others with more demanding AI workloads are adopting hybrid air and DLC.
Data center infrastructure management software is widely used but rarely utilized at full potential. Adopting the latest capabilities and optimizations could achieve better resiliency and efficiency.
Power and cooling requirements for generative AI training are upending data center design and accelerating liquid cooling adoption. Mainstream business IT will not follow until resiliency and operational concerns are addressed.
John O'Brien
Tomas Rahkonen
Max Smolaks
Andy Lawrence
Owen Rogers
Daniel Bizo
Jay Dietrich
Peter Judge
Jacqueline Davis