As Nvidia looks to capture a larger share of the server CPU market, incumbents Intel and AMD are stepping up their development efforts too. The coming years are likely to put CPU core performance back in the spotlight.
A new type of machine learning model promises greater efficiency, but only across a narrow set of tasks. Its specialised approach could offer an alternative to LLMs in specific disciplines and use-cases.
Neoclouds continue to serve as shock absorbers for the AI infrastructure buildout, now more than ever. But Nvidia, lenders and customers have taken on more risk too — if demand falls, losses may no longer stop at the neoclouds.
Data center noise is becoming a major source of complaints from nearby residents — particularly low-frequency "infrasound." New research explores the engineering challenges behind the issue and offers ways to mitigate them.
Superconductivity is still considered an exotic technology, but extreme-density IT will make data center operators take note of this option.
As AI rack power reaches hundreds of kilowatts, interest in DC power is growing. While DC can offer efficiency gains and a reduced footprint, it also introduces new challenges in protection, fault detection, grounding and worker safety.
Arc flash risks in data centers using 800V DC strongly depend on architecture — a research paper warns operators that they will need to update arc flash safety procedures to handle DC systems.
Analysis of the latest giant data center proposals from the first half of 2026 offers some useful indications of how the giant build-out is progressing.
AI can reduce data-center cooling energy consumption, but headline savings rarely tell the whole story. The opportunity lies in system-level optimization that accounts for equipment constraints, changing workloads and the shift to liquid cooling.
Meta's decision to give away its latest large language model challenges the narrative around AI infrastructure.
Access to the latest hardware and rapid speed of deployment make public cloud attractive; yet today most training workloads run on-premises.
Internal efforts to maximize token use, combined with changes in LLM pricing structures, have rapidly increased enterprise AI spending. Yet there is often limited visibility into whether such expenditures are creating value.
Vendors and consultants expect broad and rapid adoption of AI in data center management, but they may be overestimating operators' appetite for change.
Traditional air gap security presents a barrier to applications requiring live IT-OT telemetry data. Rising interest in real-time monitoring and AI-driven operations requires a rethink of outdated, inflexible cybersecurity approaches.
More data center operators are performing AI training in 2026, and this type of workload continues to be distributed across a wide range of data center venues. No single hosting model has emerged as the default.
Daniel Bizo
Max Smolaks
Dr. Owen Rogers
Peter Judge
Dr. Tomas Rahkonen
John O'Brien
Dr. Rand Talib
Douglas Donnellan