The cost of AI training varies depending on underlying infrastructure and the training approach adopted. This web tool calculates training times and workload infrastructure costs based on configurable data center, infrastructure and model attributes.
Choosing whether to train a model from scratch or fine-tune an existing one comes down to the use case and cost — with hardware utilization remaining an important cost factor.
The European Commission published the draft data center rating scheme, including a public label template, and amendments to EU 2024/1364. The regulation has been improved, but critical issues remain unaddressed.
Results from Uptime Institute's 2026 Data Center Resiliency Survey (n=1,035) focus on data center resiliency issues and the impact of outages on the data center sector globally.The attached data files below provide full results of the survey,…
The topic of direct current (DC) power distribution in data centers is back, and this time it may be different.
Although media coverage suggests data center buildout is accelerating, recent announcements and construction data paint a different picture — and suggest momentum may be slowing.
Water treatment and chemicals giant Ecolab has agreed to pay $4.75 billion in cash for Canadian DLC specialist CoolIT. It is the latest sign of unabated demand for AI compute.
Increasing community opposition to data center development is compelling hyperscalers to rethink their decades-old practices.
Enterprises deploying AI inference need to choose carefully to limit costs and protect their data.
Even before data center operators move to higher voltages, rising power densities are increasing the risks faced by electricians.
Growing public opposition to data center development has pushed many data center companies to invest in media and promotional campaigns. But, as opposition intensifies, this type of outreach is unlikely to sway public opinion.
Although cloud platforms often offer the lowest cost for AI inference, on-premises deployment may be preferable due to application architecture, data locality and control requirements.
The cost of AI inference varies widely depending on deployment model, utilization and hardware. This costing tool compares on-premises, colocation and managed AI platforms on a like-for-like basis.
PUE measures facility energy overhead relative to IT load, but it does not show how much of a site's provisioned power envelope ultimately reaches IT. Power and compute effectiveness (PCE) introduces a metric that makes this allocation visible.
Some countries are attempting to create capacity headroom for new facilities by applying efficiency limits to existing data centers. In Singapore, reducing PUE will be expensive for operators.
Dr. Owen Rogers
Jay Dietrich
Paul Carton
Anthony Sbarra
Laurie Williams
Daniel Bizo
Peter Judge
Max Smolaks
Rose Weinschenk
Dr. Rand Talib