The cost of low-carbon green hydrogen will be prohibitive for primary power for many years – but 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.
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.
The Uptime Intelligence research agenda includes a list all published and planned reports from January 2024 to March 2025, and is focused on Uptime Intelligence primary coverage areas: 1) power generation, distribution, energy storage; 2) data center...
Cloud providers need to win AI use cases in their early stages of development. If they fail to attract customers, their AI applications may be locked-in to rival platforms and harder to move, which can have serious repercussions.
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.
The cost and complexity of deploying large-scale GPU clusters for generative AI training will drive many enterprises to the public cloud. Most enterprises will use pre-trained foundation models, to reduce computational overheads.
Hydrogen from renewable sources is in short supply. While future plentiful supplies are planned, currently only a very small number of data centers are using hydrogen for standby power.
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.
While the aim of FinOps is to manage just the cloud costs, technology business management seeks to aggregate all costs of IT, including data centers, servers, software and labor, to identify savings and manage return on investment.
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.
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.
Peter Judge
Dr. Owen Rogers
Dr. Tomas Rahkonen
John O'Brien
Jacqueline Davis
Douglas Donnellan
Max Smolaks
Andy Lawrence