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Global Data Center Operator Telehouse Launches Liquid Cooling Lab in the UK to Meet Ongoing AI and HPC Demand

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Starting in early 2025, Telehouse International Corporation of Europe will offer an advanced liquid cooling lab at their newest data center, Telehouse South at the London Docklands campus in Blackwall Yard.

Telehouse has partnered with four leading liquid-cooling technology vendors — Accelsius, JetCool, Legrand, and EkkoSense — to allow customers to explore different cooling technologies and management tools while evaluating suitability for their use in the customer applications.

Dr. Stu Redshaw, Chief Technology and Innovation Officer at EkkoSense, said about the project:

Given that it’s not possible to run completely liquid-cooled data centers, the reality for most data center operators is that liquid cooling and air cooling will have an important role to play in the cooling mix – most likely as part of an evolving hybrid cooling approach. However, key engineering questions need answering before simply deploying liquid cooling – including establishing the exact blend of air and liquid cooling technologies you’ll need. And also recognizing the complexity of managing the operation of a hybrid air cooling and liquid cooling approach within the same room. This increases the need for absolute real-time white space visibility, and Telehouse’s liquid cooling lab will provide a great way to explore these issues.

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Partnering with a Range of Technology Vendors

The four companies partnering with Telehouse represent different technologies currently available in the liquid cooling market.

Accelsius:  The company will be providing its NeuCool platform. This direct-to-chip cooling solution uses a waterless, nonconductive refrigerant to remove heat and offer an environmentally friendly, platform that minimizes a data center’s water usage footprint. Designed to provide sufficient headroom for AI and HPC deployments, the system is designed to remove an average heat flux of 250W/cm2 and hot spot heat fluxes above 500W/cm2.

Included in the lab will be the Accelsius Thermal Simulation Rack, which was demonstrated at last years SuperComputing 24 conference and was announced earlier this month for a planned test deployment at a data center lab in Miami, Florida.

Josh Claman, CEO of Accelsius, remarked:

Partnering with Telehouse represents a significant milestone in our global expansion strategy. By showcasing our innovative cooling technology in one of the world’s most significant data centre hubs, we’re demonstrating our commitment to the UK market and ensuring that our cutting-edge solutions are readily accessible to potential clients across the region. As AI and other compute-intense applications continue to expand exponentially, supported by higher performance and hotter chips, our NeuCoolT system provides the headroom to manage these new, powerful accelerators, essentially future-proofing the data centre cooling system.

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JetCool:  For the Telehouse liquid cooling lab, JetCool (as recently acquired by Flex) is offering a 1U and 2U self contained liquid cooling solution that requires no piping, plumbing, or facility modifications, aka their SmartPlate System. The technology uses a microconvective liquid cooling technology and is capable of handling processors over 3500 Watts, making it suitable for all current and announced CPUs, GPUs, and AI accelerators.

The deployment in the lab will also include the JetCool  in-rack SmartSense Coolant Distribution Unit (CDU). With scalability over 2 MW per row, this CDU can be used to cool over 300 kW per rack.

Dr. Bernie Malouin, CEO of JetCool, said:

Telehouse has been a trusted partner for industries with the most demanding performance and reliability needs, including finance and telecommunications, where we already share valued customers. This partnership offers a unique opportunity to bring JetCool’s advanced liquid cooling technology to colocation customers facing power constraints, enabling greater efficiency and compute density. Together with Telehouse, we are delivering practical, high-performance cooling solutions that address real-world challenges and unlock new sustainability and performance metrics from the rack to the facility level.

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USystems:  This Legrand company will be installing their USystems ColdLogikCL20 Rear Door Heat Exchanger, which enables an equipped rack to address over 90 kW of capacity per cabinet. As displayed at the Telehouse lab, this family of RDHx units offers both active and passive cooling technologies addressing a wide range of customer demands.

Designed to address heat at its source, the technology can remove the need to explicit air mixing or containment solutions. This is done by drawing in ambient air to the rack using IT equipment fans and then moving the air via fans in the RDHx over the equipped heat exchanger, with the cooled air then moved into the equipment room. 

The process is managed by the proprietary ColdLogik adaptive intelligence which uses air-assisted liquid-cooling to efficiently control the entire room temperature. According to James Giblette, Business Unit Director, Legrand UK & Ireland:

This is a very promising partnership which provides us with another opportunity to demonstrate how our solutions-based, problem-solving, integrated approach helps the data centre industry move towards a more sustainable, energy-efficient future at a time of high-density, high-performance computing. This is a fast-changing market where coordinated innovation and comprehensive approach are what deliver results.

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EkkoSense: Telehouse already makes use of this company’s EkkoSoft Critical Platform across its London Dockyards campus, Telehouse London, which offers five collocation sites across the city. This AI-powered data center optimization software will be joined in the lab by the company’s EkkoSim ‘what-if?’ scenario simulations, air-side and liquid-side monitoring sensors, and web-based EkkoSoft Critical 3D visualisations. This AI-driven solution has already enabled the Telehouse operations team to get a better view of cooling and capacity performance, and also made the operations team more efficient in their day-to-day tasks.

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The Telehouse South data center where the liquid cooling lab is deployed is a multi-story building that offers 31,000 square meters of floor space with 2.7 MW of capacity per floor, totalling 18 MW of critical power. Each floor can support 667 industry standard racks and has dual redundant power with N+1 configured backup generators.

In discussing the company’s liquid cooling research announcement, Mark Pestridge, Executive Vice President & General Manager, Telehouse Europe, said:

Our liquid cooling lab is a very exciting partnership with three of the most innovative and experienced companies in the cutting-edge field of liquid cooling technology. We are committed to providing our customers with the best solutions that meet their needs for high levels of efficiency and sustainability.
The challenges generated by the growth of high-performance, high-density computing and AI are very significant, but we are confident that our collaborative approach and a firm focus on what is practical in an advanced data center will deliver real results and provide customers and prospects with a range of realistic options.

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Concurrent Advancements In Data Center Liquid Cooling 

As of January 2025, numerous other industry players are actively advancing liquid cooling technologies for data centers, as driven by the escalating demands of AI and HPC applications. Notable companies and organizations currently also focusing on developing and implementing innovative data center liquid cooling solutions include the following:

  • Last October, Schneider Electric announced its acquisition of a 75% controlling stake in Motivair Corp., a U.S.-based liquid cooling specialist, for approximately $850 million. This acquisition aims to enhance Schneider’s offerings in direct-to-chip liquid cooling and high-capacity thermal systems, essential for handling the increased heat generated by the growing use of generative AI and LLMs.
  • NVIDIA has been steadily advancing liquid cooling technology within its GB200 server racks, incorporating liquid cooling systems instead of traditional air cooling. This initiative is driven by the need to manage the increasing power consumption of global data centers, projected to reach 8% of total U.S. power demand by 2030.
  • Supermicro last year introduced clusters utilizing direct liquid cooling, resulting in higher performance and lower power consumption for entire data centers. The company notes this approach not only enhances efficiency but also reduces operational expenses.
  • Hewlett Packard Enterprise (HPE) has launched purpose-built solutions powered by AMD, featuring optional direct liquid cooling. The company says this option aids data centers in meeting escalating power requirements, advancing sustainability goals, and lowering operational costs.
  • The iDataCool project, a collaboration between the University of Regensburg’s physics department and IBM’s Research and Development Laboratory in Böblingen, Germany focuses on cooling IT equipment with hot water and efficiently reusing waste heat. Operating since 2011, the project serves as a research platform for innovative cooling solutions in data centers.

Keep pace with the fast-moving world of data centers and cloud computing by connecting with Data Center Frontier on LinkedIn, following us on X/Twitter and Facebook, and signing up for our weekly newsletters using the form below.

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Why DeepSeek Is Great for AI and HPC and Maybe No Big Deal for Data Centers

In the rapid and ever-evolving landscape of artificial intelligence (AI) and high-performance computing (HPC), the emergence of DeepSeek’s R1 model has sent ripples across industries. DeepSeek has been the data center industry’s topic of the week, for sure. The Chinese AI app surged to the top of US app store leaderboards last weekend, sparking a global selloff in technology shares Monday morning.  But while some analysts predict a transformative impact within the industry, a closer examination suggests that, for data centers at large, the furor over DeepSeek might ultimately be much ado about nothing. DeepSeek’s Breakthrough in AI and HPC DeepSeek, a Chinese AI startup, this month unveiled its R1 model, claiming performance on par with, or even surpassing, leading models like OpenAI’s ChatGPT-4 and Anthropic’s Claude-3.5-Sonnet. Remarkably, DeepSeek developed this model at a fraction of the cost typically associated with such advancements, utilizing a cluster of 256 server nodes equipped with 2,048 GPUs. This efficiency has been attributed to innovative techniques and optimized resource utilization. AI researchers have been abuzz about the performance of the DeepSeek chatbot that produces results similar to ChatGPT, but is based on open-source models and reportedly trained on older GPU chips. Some researchers are skeptical of claims about DeepSeek’s development costs and means, but its performance appears to challenge common assumptions about the computing cost of developing AI applications. This efficiency has been attributed to innovative techniques and optimized resource utilization.  Market Reactions and Data Center Implications The announcement of DeepSeek’s R1 model led to significant market reactions, with notable declines in tech stocks, including a substantial drop in Nvidia’s valuation. This downturn was driven by concerns that more efficient AI models could reduce the demand for high-end hardware and, by extension, the expansive data centers that house them. For now, investors are re-assessing the

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Microsoft will invest $80B in AI data centers in fiscal 2025

And Microsoft isn’t the only one that is ramping up its investments into AI-enabled data centers. Rival cloud service providers are all investing in either upgrading or opening new data centers to capture a larger chunk of business from developers and users of large language models (LLMs).  In a report published in October 2024, Bloomberg Intelligence estimated that demand for generative AI would push Microsoft, AWS, Google, Oracle, Meta, and Apple would between them devote $200 billion to capex in 2025, up from $110 billion in 2023. Microsoft is one of the biggest spenders, followed closely by Google and AWS, Bloomberg Intelligence said. Its estimate of Microsoft’s capital spending on AI, at $62.4 billion for calendar 2025, is lower than Smith’s claim that the company will invest $80 billion in the fiscal year to June 30, 2025. Both figures, though, are way higher than Microsoft’s 2020 capital expenditure of “just” $17.6 billion. The majority of the increased spending is tied to cloud services and the expansion of AI infrastructure needed to provide compute capacity for OpenAI workloads. Separately, last October Amazon CEO Andy Jassy said his company planned total capex spend of $75 billion in 2024 and even more in 2025, with much of it going to AWS, its cloud computing division.

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John Deere unveils more autonomous farm machines to address skill labor shortage

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Self-driving tractors might be the path to self-driving cars. John Deere has revealed a new line of autonomous machines and tech across agriculture, construction and commercial landscaping. The Moline, Illinois-based John Deere has been in business for 187 years, yet it’s been a regular as a non-tech company showing off technology at the big tech trade show in Las Vegas and is back at CES 2025 with more autonomous tractors and other vehicles. This is not something we usually cover, but John Deere has a lot of data that is interesting in the big picture of tech. The message from the company is that there aren’t enough skilled farm laborers to do the work that its customers need. It’s been a challenge for most of the last two decades, said Jahmy Hindman, CTO at John Deere, in a briefing. Much of the tech will come this fall and after that. He noted that the average farmer in the U.S. is over 58 and works 12 to 18 hours a day to grow food for us. And he said the American Farm Bureau Federation estimates there are roughly 2.4 million farm jobs that need to be filled annually; and the agricultural work force continues to shrink. (This is my hint to the anti-immigration crowd). John Deere’s autonomous 9RX Tractor. Farmers can oversee it using an app. While each of these industries experiences their own set of challenges, a commonality across all is skilled labor availability. In construction, about 80% percent of contractors struggle to find skilled labor. And in commercial landscaping, 86% of landscaping business owners can’t find labor to fill open positions, he said. “They have to figure out how to do

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2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

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OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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