Artificial Intelligence in Data Centre Operations

Vertiv launches AI predictive maintenance service

Vertiv, a global provider of critical digital infrastructure, has launched a new AI-powered predictive maintenance service, Vertiv Next Predict, aimed at modern data centres and facilities supporting AI workloads, including AI factories. The managed service is designed to move maintenance away from time-based and reactive models, using data analysis to identify potential issues before they affect operations. Vertiv says the service supports power, cooling, and IT systems with the aim of improving visibility and supporting more consistent infrastructure performance. The company notes that, as AI workloads increase compute intensity, data centre operators are under pressure to maintain uptime and performance across increasingly complex environments. In this respect, predictive maintenance and advanced analytics are positioned as a way to support more informed operational decisions. Ryan Jarvis, Vice President of the Global Services Business Unit at Vertiv, says, “Data centre operators need innovative technologies to stay ahead of potential risks as compute intensity rises and infrastructures evolve. “Vertiv Next Predict helps data centres unlock uptime, shifting maintenance from traditional calendar-based routines to a proactive, data-driven strategy. We move from assumptions to informed decisions by continuously monitoring equipment condition and enabling risk mitigation before potential impacts to operations.” AI-based monitoring and anomaly detection Vertiv Next Predict uses AI-based anomaly detection to analyse operating conditions and identify deviations from expected behaviour at an early stage. A predictive algorithm then assesses potential operational impact to determine risk and prioritise responses. The service also includes root cause analysis to help isolate contributing factors, supporting more targeted resolution. Based on system data and site context, prescriptive actions are defined and carried through to execution, with corrective measures carried out by Vertiv Services personnel. According to Vertiv, this approach is intended to support earlier intervention and reduce the likelihood of unplanned outages by addressing issues before they escalate. The service currently supports a range of Vertiv power and cooling platforms, including battery energy storage systems (BESS) and liquid cooling components. Vertiv says the platform is designed to expand over time to support additional technologies as data centre infrastructure evolves. Vertiv Next Predict is intended to integrate as part of a broader grid-to-chip service architecture, with the aim of supporting long-term scalability and alignment with future data centre technologies. For more from Vertiv, click here.

Vertiv predicts data centre innovation trends

Data centre innovation is continuing to be shaped by macro forces and technology trends related to AI, according to a report from Vertiv, a global provider of critical digital infrastructure. The Vertiv Frontiers report, which draws on expertise from across the organisation, details the technology trends driving current and future innovation, from powering up for AI to digital twins and adaptive liquid cooling. Scott Armul, Chief Product and Technology Officer at Vertiv, says, “The data centre industry is continuing to rapidly evolve how it designs, builds, operates, and services data centres in response to the density and speed of deployment demands of AI factories. “We see cross-technology forces, including extreme densification, driving transformative trends such as higher voltage DC power architectures and advanced liquid cooling that are important to deliver the gigawatt scaling that is critical for AI innovation. "On-site energy generation and digital twin technology are also expected to help advance the scale and speed of AI adoption.” The Vertiv Frontiers report builds on and expands Vertiv’s previous annual Data Centre Trends predictions. The report identifies macro forces driving data centre innovation. These include: • Extreme densification — accelerated by AI and HPC workloads• Gigawatt scaling at speed — with data centres now being deployed rapidly and at unprecedented scale• Data centre as a unit of compute — as the AI era requires facilities to be built and operated as a single system• Silicon diversification — noting data centre infrastructure must adapt to an increasing range of chips and compute The report details how these macro forces have in turn shaped five key trends impacting specific areas of the data centre landscape: 1. Powering up for AI Most current data centres still rely on hybrid AC/DC power distribution from the grid to the IT racks, which includes three to four conversion stages and some inefficiencies. This existing approach is under strain as power densities increase, largely driven by AI workloads. The shift to higher voltage DC architectures enables significant reductions in current, size of conductors, and number of conversion stages while centralising power conversion at the room level. Hybrid AC and DC systems are pervasive, but as full DC standards and equipment mature, higher voltage DC is likely to become more prevalent as rack densities increase. On-site generation - and microgrids - will also drive adoption of higher voltage DC. 2. Distributed AI The billions of dollars invested into AI data centres to support large language models (LLMs) to date have been aimed at supporting widespread adoption of AI tools by consumers and businesses. Vertiv believes AI is becoming increasingly critical to businesses, but how - and from where - those inference services are delivered will depend on the specific requirements and conditions of the organisation. While this will impact businesses of all types, highly regulated industries (such as finance, defence, and healthcare) may need to maintain private or hybrid AI environments via on-premise data centres, due to data residency, security, or latency requirements. Flexible, scalable high-density power and liquid cooling systems could enable capacity through new builds or retrofitting of existing facilities. 3. Energy autonomy accelerates Short-term, on-site energy generation capacity has been essential for most standalone data centres for decades to support resiliency. However, widespread power availability challenges are creating conditions to adopt extended energy autonomy, especially for AI data centres. Investment in on-site power generation, via natural gas turbines and other technologies, does have several intrinsic benefits but is primarily driven by power availability challenges. Technology strategies such as 'Bring Your Own Power (and Cooling)' are likely to be part of ongoing energy autonomy plans. 4. Digital twin-driven design and operations With increasingly dense AI workloads and more powerful GPUs also comes a demand to deploy these complex AI factories with speed. Using AI-based tools, data centres can be mapped and specified virtually - via digital twins - and the IT and critical digital infrastructure can be integrated, often as prefabricated modular designs, and deployed as units of compute, reducing time-to-token by up to 50%. This approach will be important to efficiently achieving the gigawatt-scale buildouts required for future AI advancements. 5. Adaptive, resilient liquid cooling AI workloads and infrastructure have accelerated the adoption of liquid cooling, but, conversely, AI can also be used to further refine and optimise liquid cooling solutions. Liquid cooling has become mission-critical for a growing number of operators, but AI could provide ways to further enhance its capabilities. AI, in conjunction with additional monitoring and control systems, has the potential to make liquid cooling systems smarter and even more robust by predicting potential failures and effectively managing fluid and components. This trend should lead to increasing reliability and uptime for high value hardware and associated data/workloads. For more from Vertiv, click here.

Sabey's Manhattan facility becomes AI inference hub

Sabey Data Centers, a data centre developer, owner, and operator, has said that its New York City facility at 375 Pearl Street is becoming a hub for organisations running advanced AI inference workloads. The facility, known as SDC Manhattan, offers dense connectivity, scalable power, and flexible cooling infrastructure designed to host latency-sensitive, high-throughput systems. As enterprises move from training to deployment, inference infrastructure has become critical for delivering real-time AI applications across industries. Tim Mirick, President of Sabey Data Centers, says, "The future of AI isn't just about training; it's about delivering intelligence at scale. Our Manhattan facility places that capability at the edge of one of the world's largest and most connected markets. "That's an enormous advantage for inference models powering everything from financial services to media to healthcare." Location and infrastructure Located within walking distance of Wall Street and major carrier hotels, SDC Manhattan is among the few colocation providers in Manhattan with available power. The facility has nearly one megawatt of turnkey power available and seven megawatts of utility power across two powered shell spaces. The site provides access to numerous network providers as well as low-latency connectivity to major cloud on-ramps and enterprises across the Northeast. Sabey says it offers organisations the ability to deploy inference clusters close to their users, reducing response times and enabling real-time decision-making. The facility's liquid-cooling-ready infrastructure supports hybrid cooling configurations to accommodate GPUs and custom accelerators. For more from Sabey Data Centers, click here.

Europe races to build its own AI backbone

Recent outages across global cloud infrastructure have once again served as a reminder of how deeply Europe depends on foreign hyperscalers. When platforms run on AWS or services protected by Cloudflare fail, European factories, logistics hubs, retailers, and public services can stall instantly. US-based cloud providers currently dominate Europe’s infrastructure landscape. According to market data, Amazon, Microsoft, and Google together control roughly 70% of Europe’s public cloud market. In contrast, all European providers combined account for only about 15%. This share has declined sharply over the past decade. For European enterprises, this means limited leverage over resilience, performance, data governance, and long-term sovereignty. This same structural dependency is now extending from cloud infrastructure directly into artificial intelligence and its underlying investments. Between 2018 and 2023, US companies attracted more than €120 billion (£104 billion) in private AI investment, while the European Union drew about €32.5 billion (£28 billion) over the same period. In 2024 alone, US-based AI firms raised roughly $109 billion (£81 billion), more than six times the total private AI investment in Europe that year. Europe is therefore trying to close the innovation gap while simultaneously tightening regulation, creating a paradox in which calls for digital sovereignty grow louder even as reliance on non-European infrastructure deepens. The European Union’s Apply AI Strategy is designed to move AI out of research environments and into real industrial use, backed by more than one billion euros in funding. However, most of the computing power, cloud platforms, and model infrastructure required to deploy these systems at scale still comes from outside Europe. This creates a structural risk: even as AI adoption accelerates inside European industry, much of the strategic control over its operation may remain in foreign hands. Why industrial AI is Europe’s real monitoring ground For any large-scale technology strategy to succeed, it must be tested and refined through real-world deployment, not only shaped at the policy level. The effectiveness of Europe’s AI push will ultimately depend on how quickly new rules, funding mechanisms, and technical standards translate into working systems, and how fast feedback from practice can inform the next iteration. This is where industrial environments become especially important. They produce large amounts of real-time data, and the results of AI use are quickly visible in productivity and cost. As a result, industrial AI is becoming one of the main testing grounds for Europe’s AI ambitions. The companies applying AI in practice will be the first to see what works, what does not, and what needs to be adjusted. According to Giedrė Rajuncė, CEO and co-founder of GREÏ, an AI-powered operational intelligence platform for industrial sites, this shift is already visible on the factory floor, where AI is changing how operations are monitored and optimised in real time. She notes, “AI can now monitor operations in real time, giving companies a new level of visibility into how their processes actually function. I call it a real-time revolution, and it is available at a cost no other technology can match. Instead of relying on expensive automation as the only path to higher effectiveness, companies can now plug AI-based software into existing cameras and instantly unlock 10–30% efficiency gains.” She adds that Apply AI reshapes competition beyond technology alone, stating, “Apply AI is reshaping competition for both talent and capital. European startups are now competing directly with US giants for engineers, researchers, and investors who are increasingly focused on industrial AI. From our experience, progress rarely starts with a sweeping transformation. It starts with solving one clear operational problem where real-time detection delivers visible impact, builds confidence, and proves return on investment.” The data confirms both movement and caution. According to Eurostat, 41% of large EU enterprises had adopted at least one AI-based technology in 2024. At the same time, a global survey by McKinsey & Company shows that 88% of organisations worldwide are already using AI in at least one business function. “Yes, the numbers show that Europe is still moving more slowly,” Giedrė concludes. “But they also show something even more important. The global market will leave us no choice but to accelerate. That means using the opportunities created by the EU’s push for AI adoption before the gap becomes structural.”

Study finds consumer GPUs can cut AI inference costs

A peer-reviewed study has found that consumer-grade GPUs, including Nvidia’s RTX 4090, can significantly reduce the cost of running large language model (LLM) inference. The research, published by io.net - a US developer of decentralised GPU cloud infrastructure - and accepted for the 6th International Artificial Intelligence and Blockchain Conference (AIBC 2025), provides the first open benchmarks of heterogeneous GPU clusters deployed on the company’s decentralised cloud platform. The paper, Idle Consumer GPUs as a Complement to Enterprise Hardware for LLM Inference, reports that clusters built from RTX 4090 GPUs can deliver between 62% and 78% of the throughput of enterprise-grade H100 hardware at roughly half the cost. For batch processing or latency-tolerant workloads, token costs fell by up to 75%. The study also notes that, while H100 GPUs remain more energy efficient on a per-token basis, extending the life of existing consumer hardware and using renewable-rich grids can reduce overall emissions. Aline Almeida, Head of Research at IOG Foundation and lead author of the study, says, “Our findings demonstrate that hybrid routing across enterprise and consumer GPUs offers a pragmatic balance between performance, cost, and sustainability. "Rather than a binary choice, heterogeneous infrastructure allows organisations to optimise for their specific latency and budget requirements while reducing carbon impact.” Implications for LLM development and deployment The research outlines how AI developers and MLOps teams can use mixed hardware clusters to improve cost-efficiency. Enterprise GPUs can support real-time applications, while consumer GPUs can be deployed for batch tasks, development, overflow capacity, and workloads with higher latency tolerance. Under these conditions, the study reports that organisations can achieve near-H100 performance with substantially lower operating costs. Gaurav Sharma, CEO of io.net, comments, “This peer-reviewed analysis validates the core thesis behind io.net: that the future of compute will be distributed, heterogeneous, and accessible. "By harnessing both data-centre-grade and consumer hardware, we can democratise access to advanced AI infrastructure while making it more sustainable.” The company also argues that the study supports its position that decentralised networks can expand global compute capacity by making distributed GPU resources available to developers through a single, programmable platform. Key findings include: • Cost-performance ratios — Clusters of four RTX 4090 GPUs delivered 62% to 78% of H100 throughput at around half the operational cost, achieving the lowest cost per million tokens ($0.111–0.149). • Latency profiles — H100 hardware maintained sub-55ms P99 time-to-first-token even at higher loads, while consumer GPU clusters were suited to workloads tolerating 200–500ms tail latencies, such as research, development environments, batch jobs, embeddings, and evaluation tasks.

UK Chancellor urged to use AI for economic growth

UK Chancellor Rachel Reeves has been urged to use the opportunity afforded by AI to ‘Make Britain Great Again’. The news comes as the Government announced that thousands of new AI jobs and billions of pounds of investment will be poured into the next parliament to help stimulate economic growth. New AI Growth Zones Amongst the package of measures proposed for the budget today include a new AI Growth Zone in South Wales, which will create more than 5,000 new jobs for local communities over the next decade, and a further £137 million to support key scientists to drive breakthroughs and develop new drugs, cures, and treatments. Patrick Sullivan, CEO of think tank Parliament Street, argued that with limited options at the budget, only AI can ‘Make Britain Great Again’. He claims, “With limited options due to Labour’s absurd manifesto pledge to rule out income tax rises, the Chancellor is now forced to cobble together a quick fix solution to fill a black hole which is entirely of her own making. "However, the one saving grace is the advent of mass AI adoption, a technology that will bring mass savings at a time when the Government needs it most. “This is Labour’s chance to show that it gets private enterprise and recognises that by supporting tech talent, AI can truly Make Britain Great Again.” Tech expert Graeme Stewart, Head of Public Sector at Check Point Software, says, “The case for investing billions in AI to drive growth and reboot the economy is clear, yet little has been said about the cyber and regulatory risks associated with mass adoption. “Whether it’s attacks on the NHS, nurseries, or local councils, cyber criminals have already proven that nothing in the public sector is off limits. That’s why it’s vital the Chancellor’s AI rollout is backed up with a robust action plan for protecting critical national infrastructure and minimising cyber risk. "We also need to hear more about the Government’s plans to protect the public and private sector from the new wave of AI-enabled cyber-attacks, which require a cohesive national strategy.” Graeme continues, “Mastering AI to drive growth is the right thing to do, but this approach must always go hand-in-hand with the necessary cyber strategy, to ensure the government stays one step ahead of the increasingly lethal cyber threat.” Kenny MacAulay, CEO of Acting Office, a software platform for accounting practices, adds, “With businesses still reeling from the £25 billion National Insurance increase, the Chancellor has a tough task ahead to win the back trust from the private sector. "Proposals for a nationwide AI rollout and investment in infrastructure can help kickstart economic growth, but only alongside a clear action plan to get businesses hiring again. “The industry needs to embrace the opportunities that AI can bring, in terms of centralising technology investment and improving customer service.”

UK Government unveils major AI investment package

The UK Government has announced a comprehensive package of AI-focused reforms and investments to accelerate national renewal, boost economic growth, and cement the UK’s position as a global leader in artificial intelligence. The new investment places AI at the centre of the UK’s Modern Industrial Strategy, unlocking billions in private investment and enabling new opportunities for businesses, researchers, and local communities across the country. AI Growth Zones A new AI Growth Zone in South Wales, developed with Vantage Data Centers and Microsoft, will receive £10 billion in private investment and create more than 5,000 jobs over the next decade. Spanning multiple sites along the M4 corridor, including the former Ford Bridgend Engine Plant, the zone will serve as a major hub for AI infrastructure, research, and advanced digital industries. Each AI Growth Zone will benefit from £5 million in government support to help local businesses adopt AI technologies and develop specialised skills in their workforce. Sachin Agrawal, Managing Director for Zoho UK, comments, "The UK’s bold commitment to harnessing artificial intelligence for national renewal is both timely and visionary. This investment represents a crucial step towards ensuring the benefits of AI and data innovation are distributed fairly across the country. "For businesses, the real opportunity lies not only in adopting AI tools, but in developing the skills, governance, and readiness to apply them responsibly at scale. Keeping data privacy at the centre of any AI strategy creates the right foundation to make informed decisions and adopt AI responsibly. "AI literacy and strong data protection standards will be essential to ensure initiatives are credible and built for long‑term impact. Structured implementation, starting with clearly defined pilot programmes underpinned by automation, governance, and security, will help businesses move beyond experimentation and ensure AI drives sustainable competitive advantage. "With the right guidance and accountability in place, AI can support transformative growth across the UK.” To keep UK firms at the front of global AI capability, the Government is launching a new programme to expand free and low-cost compute access. Up to £250 million will be deployed to help British researchers and startups train models and pursue scientific breakthroughs. Alongside this, a new advance market commitment, worth up to £100 million, will allow the Government to act as an early customer for UK AI hardware startups, supporting domestic chip innovation and ensuring British-designed hardware plays a role in future data centre deployments.

XYZ Reality, Applied Digital partner on 400MW campus

XYZ Reality, a provider of augmented reality (AR) and real-time project controls, is supporting high-performance data centre operator Applied Digital’s delivery of an AI factory in Ellendale, North Dakota. The 400-megawatt (MW) Ellendale AI Factory Campus leverages North Dakota’s cool climate and renewable energy to create a sustainable foundation for advanced computing. XYZ Reality’s construction delivery platform, supported by its team of site engineers, is helping Applied Digital’s project teams track progress in real time, validate installations, and maintain quality standards throughout the build. As part of the partnership, XYZ Reality’s site engineers are embedded on-site to provide verified build progress, installation accuracy, and proactive quality assurance aligned with project plans. Construction of an AI factory David Mitchell, Founder & CEO of XYZ Reality, comments, “Applied Digital is redefining what’s possible in AI infrastructure and it’s exciting to be part of that journey. "From day one, our teams have clicked through a shared drive to push boundaries and use technology differently. Together, we’re proving that transparency, precision, and data-led delivery can transform how these massive projects come to life.” Waleed Zafar, CRO at XYZ Reality, adds, “Working alongside Tier 1 developers like Applied Digital, we’re demonstrating the true impact of data-led construction. "Our platform gives project teams complete visibility and confidence from the ground up - driving precision, accountability, and measurable performance improvements across delivery. "Having already been deployed on more than 2.5GW of data centres, we’re proud to be setting a new standard for how mission-critical infrastructure is built.” For more from XYZ Reality, click here.

AI rush deemed "incompatible" with Clean Power Plan

A forecast suggests that the UK data centre boom is at odds with the UK’s clean power commitments, with the sector already overwhelming the electricity system and forcing an unavoidable reliance on gas. This is the view put forward by Simon Gallagher, Managing Director at UK Networks Services, speaking at Montel's UK Energy Day event earlier today (13 November). Simon said that only firm capacity be counted on in the context of powering data centres, as adverse weather conditions would reduce the availability of wind and solar during periods of low wind and sunlight. When asked what could realistically power tens of gigawatts worth of near constant data centre load, Simon said his “controversial opinion” was that this demand “is going to have to be met by gas.” “It’s the only technology we have that can do this on a firm basis. We don’t have storage,” Simon added. This led him to conclude that the sector’s growth was simply not compatible with the UK government’s Clean Power 2030 plans, under which 95% of energy must come from low-carbon sources by the turn of the decade. Physical limitations This comes amid an explosion in grid connection requests, jumping from around 17 GW to 97 GW over the summer, pushing the total capacity waiting for connections to the UK grid up to 125 GW. Simon continued, “About 80% of that is hyperscale data centres. It’s all AI. The impact on our grid is very real – and it just happened.” The UK was “never, ever” going to build the required transmission capacity in time, Simon added, with a new connection taking “at least five years.” He also outlined how the infrastructure available is not where data centres want to be, adding that such facilities seek sizeable connections “at transmission voltage, in urban areas near fibre.” This would typically site them away from significant power generation zones, which could help to alleviate network constraints and reduce balancing costs. Earlier today, Dhara Vyas, CEO of trade group Energy UK, told the same event that the UK’s clean energy expansion was being slowed by planning rules and grid connection queues that were “actively deterring investors”. For more from Montel, click here.

VAST Data, CoreWeave agree $1.17 billion partnership

VAST Data, an AI operating system company, has announced a $1.17 billion (£889.8 million) commercial agreement with CoreWeave, a US provider of GPU-based cloud computing infrastructure for AI workloads, to extend their existing partnership in AI data infrastructure. The deal formalises CoreWeave’s use of the VAST Data Operating System (AI OS) as a key element of its data management platform. Expanding collaboration on large-scale data operations CoreWeave’s infrastructure, which uses the VAST AI OS, is designed to provide rapid access to large datasets and support intensive AI workloads. Its modular architecture allows deployment across multiple data centres, maintaining performance and reliability across distributed environments. As part of the agreement, VAST and CoreWeave will collaborate on new data services intended to improve efficiency in data pipelines and model development. The partnership aims to enhance operational consistency and reduce complexity for enterprise users developing or training AI models at scale. “At VAST, we are building the data foundation for the most ambitious AI initiatives in the world,” claims Renen Hallak, founder and CEO of VAST Data. “Our deep integration with CoreWeave is the result of a long-term commitment to working side by side at both the business and technical level. "By aligning our roadmaps, we are delivering an AI platform that organisations cannot find anywhere else in the market.” “The VAST AI Operating System underpins key aspects of how we design and deliver our AI cloud,” adds Brian Venturo, co-founder and Chief Strategy Officer of CoreWeave. “This partnership enables us to deliver AI infrastructure that is the most performant, scalable, and cost-efficient in the market, while reinforcing the trust and reliability of a data platform that our customers depend on for their most demanding workloads.” Supporting next-generation AI and compute systems Both companies say this agreement reflects their joint focus on developing infrastructure that can manage large-scale data processing and continuous AI training. By integrating VAST’s data management systems with CoreWeave’s GPU-based infrastructure, the partnership aims to support use cases such as real-time inference and industrial-scale model training. For more from VAST Data, click here.