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.
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.
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.
This report provides a regional view of the results from the Uptime Institute Global Data Center Survey 2024 and highlights some of the different challenges and strategies of data center owners and operators across the globe.
Trust in AI as a tool for data center operations has declined sharply in the past three years. It is possible to control for the factors that drive mistrust — and see better outcomes when employees interact with AI-based systems.
Hydrogen is a promising energy storage medium that can help decarbonize infrastructure. It is not a great fit for the majority of data centers, and the hydrogen economy is not fully developed.
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?
Two-phase immersion was expected to revolutionize data center cooling but proved difficult to implement. With escalating silicon thermal power, two-phase is gaining substantial interest again, just in a different form: direct-to-chip liquid cooling.