Akamai extends its micro-segmentation and zero-trust security platform Guardicore to run on Nvidia BlueField GPUs The integration offloads user-configurable security processes from the host system to the Nvidia BlueField DPU and enables zero-trust segmentation without requiring software agents on fragile or legacy systems, according to Akamai. Organizations can implement this hardware-isolated, “agentless” security approach to help align with regulatory requirements and lower their risk profile for cyber insurance. “It delivers deep, out-of-band visibility across systems, networks, and applications without disrupting operations. Security policies can be enforced in real time and are capable of creating a strong protective boundary around critical operational systems. The result is trusted insight into operational activity and improved overall cyber resilience,” according to Akamai. Forescout works with Nvidia to bring zero-trust technology to OT networks Forescout applies network segmentation to contain lateral movement and enforce zero-trust controls. The technology would be further integrated into partnership work already being done by the two companies. By running Forescout’s on-premises sensor directly on the Nvidia BlueField, part of Nvidia Cybersecurity AI platform, customers can offload intensive computing tasks, such as deep packet inspections. This speeds up data processing, enhances asset intelligence, and improves real-time monitoring, providing security teams with the insights needed to stay ahead of emerging threats, according to Forescout. Palo Alto to demo Prisma AIRS AI Runtime Security on Nvidia BlueField DPU Palo Alto Networks recently partnered with Nvidia to run its Prisma AI-powered Radio Security(AIRs) package on the Nvidia BlueField DPU and will show off the technology at the conference. The technology is part of the Nvidia Enterprise AI Factory validated design and can offer real-time security protection for industrial network settings. “Prisma AIRS AI Runtime Security delivers deep visibility into industrial traffic and continuous monitoring for abnormal behavior. By running these security services on Nvidia BlueField, inspection

IBM isn’t the only one. “We’ve been doing liquid cooling since 2012 on our supercomputers,” says Scott Tease, vice president and general manager of AI and high-performance computing at Lenovo’s infrastructure solutions group. “And we’ve been improving it ever since—we’re now on the sixth generation of that technology.” And the liquid Lenovo uses in its Neptune liquid cooling solution is warm water. Or, more precisely, hot water: 45 degrees Celsius. And when the water leaves the servers, it’s even hotter, Tease says. “I don’t have to chill that water, even if I’m in a hot climate,” he says. Even at high temperatures, the water still provides enough cooling to the chips that it has real value. “Generally, a data center will use evaporation to chill water down,” Tease adds. “Since we don’t have to chill the water, we don’t have to use evaporation. That’s huge amounts of savings on the water. For us, it’s almost like a perfect solution. It delivers the highest performance possible, the highest density possible, the lowest power consumption. So, it’s the most sustainable solution possible.” So, how is the water cooled down? It gets piped up to the roof, Tease says, where there are giant radiators with massive amounts of surface area. The heat radiates away, and then all the water flows right back to the servers again. Though not always. The hot water can also be used to, say, heat campus or community swimming pools. “We have data centers in the Nordics who are giving the heat to the local communities’ water systems,” Tease says.

2) “Units of compute”: OneCore and SmartRun On the earnings call, Albertazzi highlighted Vertiv OneCore, an end-to-end data center solution designed to accelerate “time to token,” scaling in 12.5 MW building blocks; and Vertiv SmartRun, a prefabricated white space infrastructure solution aimed at rapidly accelerating fit-out and readiness. He pointed to collaborations (including Hut 8 and Compass Data Centers) as proof points of adoption, emphasizing that SmartRun can stand alone or plug into OneCore. 3) Cooling evolution: hybrid thermal chains and the “trim cooler” Asked how cooling architectures may change (amid industry chatter about warmer-temperature operations and shifting mixes of chillers, CDUs, and other components) Albertazzi leaned into complexity as a feature, not a bug. He argued heat rejection doesn’t disappear, even if some GPU loads can run at higher temperatures. Instead, the future looks hybrid, with mixed loads and resiliency requirements forcing more nuanced thermal chains. Vertiv’s strategic product anchor here is its “trim cooler” concept: a chiller optimized for higher-temperature operation while retaining flexibility for lower-temperature requirements in the same facility, maximizing free cooling where climate and design allow. And importantly, Albertazzi dismissed the idea that CDUs are going away: “We are pretty sure that CDUs in various shapes and forms are a long-term element of the thermal chain.” 4) Edge densification: CoolPhase Ceiling + CoolPhase Row (Feb. 3) Vertiv also expanded its thermal portfolio for edge and small IT environments with the: Vertiv CoolPhase Ceiling (launching Q2 2026): ceiling-mounted, 3.5 kW to 28 kW, designed to preserve floor space. Vertiv CoolPhase Row (available now in North America) for row-based cooling up to 30 kW (300 mm width) or 40 kW (600 mm width). Vertiv Director of Edge Thermal Michal Podmaka tied the products directly to AI-driven edge densification and management consistency, saying the new systems “integrate seamlessly

DCF founder Rich Miller has spent much of his career explaining how the data center industry works. Now, with his latest venture, Data Center Richness, he’s also examining how the industry learns. That thread provided the opening for the latest episode of The DCF Show Podcast, where Miller joined present Data Center Frontier Editor in Chief Matt Vincent and Senior Editor David Chernicoff for a wide-ranging discussion that ultimately landed on a simple conclusion: after two years of unprecedented AI-driven announcements, 2026 will be the year reality asserts itself. Projects will either get built, or they won’t. Power will either materialize, or it won’t. Communities will either accept data center expansion – or they’ll stop it. In other words, the industry is entering its execution phase. Why Data Center Richness Matters Now Miller launched Data Center Richness as both a podcast and a Substack publication, an effort to experiment with formats and better understand how professionals now consume industry information. Podcasts have become a primary way many practitioners follow the business, while YouTube’s discovery advantages increasingly make video versions essential. At the same time, Miller remains committed to written analysis, using Substack as a venue for deeper dives and format experimentation. One example is his weekly newsletter distilling key industry developments into just a handful of essential links rather than overwhelming readers with volume. The approach reflects a broader recognition: the pace of change has accelerated so much that clarity matters more than quantity. The topic of how people learn about data centers isn’t separate from the industry’s trajectory; it’s becoming part of it. Public perception, regulatory scrutiny, and investor expectations are now shaped by how stories are told as much as by how facilities are built. That context sets the stage for the conversation’s core theme. Execution Defines 2026 After

Back in September 2025, we examined an ambitious proposal from infrastructure developer Joule Capital Partners – often branding the effort as “Joule Power” – in partnership with Caterpillar. The concept is straightforward but consequential: acquire a vast rural tract in Millard County, Utah, and pair an AI-focused data center campus with large-scale, on-site “behind-the-meter” generation to bypass the interconnection queues, transmission constraints, and substation bottlenecks slowing projects nationwide. The appeal is clear: speed-to-power and greater control over delivery timelines. But that speed shifts the project’s risk profile. Instead of navigating traditional utility procurement, the development begins to resemble a distributed power plant subject to industrial permitting, fuel supply logistics, air emissions scrutiny, noise controls, and groundwater governance. These are issues communities typically associate with generation facilities, not hyperscale data centers. Our earlier coverage focused on the technical and strategic logic of pairing compute with on-site generation. Now the story has evolved. Community opposition is emerging as a material variable that could influence schedule and scope. Although groundbreaking was held in November 2025, final site plans and key conditional use permits remain pending at the time of publication. What Is Actually Being Proposed? Public records from Millard County show Joule pursuing a zone change for approximately 4,000 acres (about 6.25 square miles), converting agricultural land near 11000 N McCornick Road to Heavy Industrial use. At a July 2025 public meeting, residents raised familiar concerns that surface when a rural landscape is targeted for hyperscale development: labor influx and housing strain, water use, traffic, dust and wildfire risk, wildlife disruption, and the broader loss of farmland and local character. What has proven less clear is the precise scale and sequencing of the buildout. Local reporting describes an initial phase of six data center buildings, each supported by a substantial fleet of Caterpillar

The Production Readiness Gap AI teams continue to confront a familiar challenge: moving from experimentation to predictable production performance. Models that train successfully on small clusters or sandbox environments often behave very differently when deployed at scale. Performance characteristics shift. Data pipelines strain under sustained load. Cost assumptions unravel. Synthetic benchmarks and reduced test sets rarely capture the complex interactions between compute, storage, networking, and orchestration that define real-world AI systems. The result can be an expensive “Day One” surprise:  unexpected infrastructure costs, bottlenecks across distributed components, and delays that ripple across product timelines. CoreWeave’s view is that benchmarking and production launch can no longer be treated as separate phases. Instead, validation must occur in environments that replicate the architectural, operational, and economic realities of live deployment. ARENA is designed around that premise. The platform allows customers to run full workloads on CoreWeave’s production-grade GPU infrastructure, using standardized compute stacks, network configurations, data paths, and service integrations that mirror actual deployment environments. Rather than approximating production behavior, the goal is to observe it directly. Key capabilities include: Running real workloads on GPU clusters that match production configurations. Benchmarking both performance and cost under realistic operational conditions. Diagnosing bottlenecks and scaling behavior across compute, storage, and networking layers. Leveraging standardized observability tools and guided engineering support. CoreWeave positions ARENA as an alternative to traditional demo or sandbox environments; one informed by its own experience operating large-scale AI infrastructure. By validating workloads under production conditions early in the lifecycle, teams gain empirical insight into performance dynamics and cost curves before committing capital and operational resources. Why Production-Scale Validation Has Become Strategic The demand for environments like ARENA reflects how fundamentally AI workloads have changed. Several structural shifts are driving the need for production-scale validation: Continuous, Multi-Layered Workloads AI systems are no longer