“Personally, I think that a brownfield is very creative way to deal with what I think is the biggest problem that we’ve got right now, which is time and speed to market,” he said. “On a brownfield, I can go into a building that’s already got power coming into the building. Sometimes they’ve already got chiller plants, like what we’ve got with the building I’m in right now.” Patmos certainly made the most of the liquid facilities in the old printing press building. The facility is built to handle anywhere from 50 to over 140 kilowatts per cabinet, a leap far beyond the 1–2 kW densities typical of legacy data centers. The chips used in the servers are Nvidia’s Grace Blackwell processors, which run extraordinarily hot. To manage this heat load, Patmos employs a multi-loop liquid cooling system. The design separates water sources into distinct, closed loops, each serving a specific function and ensuring that municipal water never directly contacts sensitive IT equipment. “We have five different, completely separated water loops in this building,” said Morgan. “The cooling tower uses city water for evaporation, but that water never mixes with the closed loops serving the data hall. Everything is designed to maximize efficiency and protect the hardware.” The building taps into Kansas City’s district chilled water supply, which is sourced from a nearby utility plant. This provides the primary cooling resource for the facility. Inside the data center, a dedicated loop circulates a specialized glycol-based fluid, filtered to extremely low micron levels and formulated to be electronically safe. Heat exchangers transfer heat from the data hall fluid to the district chilled water, keeping the two fluids separate and preventing corrosion or contamination. Liquid-to-chip and rear-door heat exchangers are used for immediate heat removal.

Why This Project Marks a Landmark Shift The deployment of 2.3 GW of modular generation represents utility-scale capacity, but what makes it distinct is the delivery model. Instead of a centralized plant, the project uses modular gas-reciprocating “power packs” that can be phased in step with data-hall readiness. This approach allows staged energization and limits the bottlenecks that often stall AI campuses as they outgrow grid timelines or wait in interconnection queues. AI training loads fluctuate sharply, placing exceptional stress on grid stability and voltage quality. The INNIO/VoltaGrid platform was engineered specifically for these GPU-driven dynamics, emphasizing high transient performance (rapid load acceptance) and grid-grade power quality, all without dependence on batteries. Each power pack is also designed for maximum permitting efficiency and sustainability. Compared with diesel generation, modern gas-reciprocating systems materially reduce both criteria pollutants and CO₂ emissions. VoltaGrid markets the configuration as near-zero criteria air emissions and hydrogen-ready, extending allowable runtimes under air permits and making “prime-as-a-service” viable even in constrained or non-attainment markets. 2025: Momentum for Modular Prime Power INNIO has spent 2025 positioning its Jenbacher platform as a next-generation power solution for data centers: combining fast start, high transient performance, and lower emissions compared with diesel. While the 3 MW J620 fast-start lineage dates back to 2019, this year the company sharpened its data center narrative and booked grid stability and peaking projects in markets where rapid data center growth is stressing local grids. This momentum was exemplified by an 80 MW deployment in Indonesia announced earlier in October. The same year saw surging AI-driven demand and INNIO’s growing push into North American data-center markets. Specifications for the 2.3 GW VoltaGrid package highlight the platform’s heat tolerance, efficiency, and transient response, all key attributes for powering modern AI campuses. VoltaGrid’s 2025 Milestones VoltaGrid’s announcements across 2025 reflect

Matt Kimball, VP and principal analyst with Moor Insights and Strategy, pointed out that AWS and Microsoft have already moved many workloads from x86 to internally designed Arm-based servers. He noted that, when Arm first hit the hyperscale datacenter market, the architecture was used to support more lightweight, cloud-native workloads with an interpretive layer where architectural affinity was “non-existent.” But now there’s much more focus on architecture, and compatibility issues “largely go away” as Arm servers support more and more workloads. “In parallel, we’ve seen CSPs expand their designs to support both scale out (cloud-native) and traditional scale up workloads effectively,” said Kimball. Simply put, CSPs are looking to monetize chip investments, and this migration signals that Google has found its performance-per-dollar (and likely performance-per-watt) better on Axion than x86. Google will likely continue to expand its Arm footprint as it evolves its Axion chip; as a reference point, Kimball pointed to AWS Graviton, which didn’t really support “scale up” performance until its v3 or v4 chip. Arm is coming to enterprise data centers too When looking at architectures, enterprise CIOs should ask themselves questions such as what instance do they use for cloud workloads, and what servers do they deploy in their data center, Kimball noted. “I think there is a lot less concern about putting my workloads on an Arm-based instance on Google Cloud, a little more hesitance to deploy those Arm servers in my datacenter,” he said. But ultimately, he said, “Arm is coming to the enterprise datacenter as a compute platform, and Nvidia will help usher this in.” Info-Tech’s Jain agreed that Nvidia is the “biggest cheerleader” for Arm-based architecture, and Arm is increasingly moving from niche and mobile use to general-purpose and AI workload execution.

What 6 GW of GPUs Really Means The 6 GW of accelerator load envisioned under the OpenAI–AMD partnership will be distributed across multiple hyperscale AI factory campuses. If OpenAI begins with 1 GW of deployment in 2026, subsequent phases will likely be spread regionally to balance supply chains, latency zones, and power procurement risk. Importantly, this represents entirely new investment in both power infrastructure and GPU capacity. OpenAI and its partners have already outlined multi-GW ambitions under the broader Stargate program; this new initiative adds another major tranche to that roadmap. Designing for the AI Factory Era These upcoming facilities are being purpose-built for next-generation AI factories, where MI450-class clusters could drive rack densities exceeding 100 kW. That level of compute concentration makes advanced power and cooling architectures mandatory, not optional. Expected solutions include: Warm-water liquid cooling (manifold, rear-door, and CDU variants) as standard practice. Facility-scale water loops and heat-reuse systems—including potential district-heating partnerships where feasible. Medium-voltage distribution within buildings, emphasizing busway-first designs and expanded fault-current engineering. While AMD has not yet disclosed thermal design power (TDP) specifications for the MI450, a 1 GW campus target implies tens of thousands of accelerators. That scale assumes liquid cooling, ultra-dense racks, and minimal network latency footprints, pushing architectures decisively toward an “AI-first” orientation. Design considerations for these AI factories will likely include: Liquid-to-liquid cooling plants engineered for step-function capacity adders (200–400 MW blocks). Optics-friendly white space layouts with short-reach topologies, fiber raceways, and aisles optimized for module swaps. Substation adjacency and on-site generation envelopes negotiated during early land-banking phases. Networking, Memory, and Power Integration As compute density scales, networking and memory bottlenecks will define infrastructure design. Expect fat-tree and dragonfly network topologies, 800 G–1.6 T interconnects, and aggressive optical-module roadmaps to minimize collective-operation latency, aligning with recent disclosures from major networking vendors.

In a new report spanning 2022 through 2024, the Union of Concerned Scientists (UCS) identifies a significant regulatory gap in the PJM Interconnection’s planning and rate-making process—one that allows most high-voltage (“transmission-level”) interconnection costs for large, especially AI-scale, data centers to be socialized across all utility customers. The result, UCS argues, is a multi-billion-dollar pass-through that is poised to grow as more data center projects move forward, because these assets are routinely classified as ordinary transmission infrastructure rather than customer-specific hookups. According to the report, between 2022 and 2024, utilities initiated more than 150 local transmission projects across seven PJM states specifically to serve data center connections. In 2024 alone, 130 projects were approved with total costs of approximately $4.36 billion. Virginia accounted for nearly half that total—just under $2 billion—followed by Ohio ($1.3 billion) and Pennsylvania ($492 million) in data-center-related interconnection spending. Yet only six of those 130 projects, about 5 percent, were reported as directly paid for by the requesting customer. The remaining 95 percent, representing more than $4 billion in 2024 connection costs, were rolled into general transmission charges and ultimately recovered from all retail ratepayers. How Does This Happen? When data center project costs are discussed, the focus is usually on the price of the power consumed, or megawatts multiplied by rate. What the UCS report isolates, however, is something different: the cost of physically delivering that power: the substations, transmission lines, and related infrastructure needed to connect hyperscale facilities to the grid. So why aren’t these substantial consumer-borne costs more visible? The report identifies several structural reasons for what effectively functions as a regulatory loophole in how development expenses are reported and allocated: Jurisdictional split. High-voltage facilities fall under the Federal Energy Regulatory Commission (FERC), while retail electricity rates are governed by state public utility

With the conclusion of the 2025 OCP Global Summit, William G. Wong, Senior Content Director at DCF’s sister publications Electronic Design and Microwaves & RF, published a comprehensive roundup of standout technologies unveiled at the event. For Data Center Frontier readers, we’ve revisited those innovations through the lens of data center impact, focusing on how they reshape infrastructure design and operational strategy. This year’s OCP Summit marked a decisive shift toward denser GPU racks, standardized direct-to-chip liquid cooling, 800-V DC power distribution, high-speed in-rack fabrics, and “crypto-agile” platform security. Collectively, these advances aim to accelerate time-to-capacity, reduce power-distribution losses at megawatt rack scales, simplify retrofits in legacy halls, and fortify data center platforms against post-quantum threats. Rack Design and Cooling: From Ad-Hoc to Production-Grade Liquid Cooling NVIDIA’s Vera Rubin compute tray, newly offered to OCP for standardization, packages Rubin-generation GPUs with an integrated liquid-cooling manifold and PCB midplane. Compared with the GB300 tray, Vera Rubin represents a production-ready module delivering four times the memory and three times the memory bandwidth: a 7.5× performance factor at rack scale, with 150 TB of memory at 1.7 PB/s per rack. The system implements 45 °C liquid cooling, a 5,000-amp liquid-cooled busbar, and on-tray energy storage with power-resilience features such as flexible 100-amp whips and automatic-transfer power-supply units. NVIDIA also previewed a Kyber rack generation targeted for 2027, pivoting from 415/480 VAC to 800 V DC to support up to 576 Rubin Ultra GPUs, potentially eliminating the 200-kg copper busbars typical today. These refinements are aimed at both copper reduction and aisle-level manageability. Wiwynn’s announcements filled in the practicalities of deploying such densities. The company showcased rack- and system-level designs across NVIDIA GB300 NVL72 (72 Blackwell Ultra GPUs with 800 Gb/s ConnectX-8 SuperNICs) for large-scale inference and reasoning, and HGX B300 (eight GPUs /