The 100 kW rack figure places MSI’s offering squarely in the world of AI-era rack densities, where conventional air cooling becomes increasingly difficult or inefficient. The announcement also suggests that MSI is aligning with hyperscale and large cloud design principles, particularly through ORv3 and 48V power distribution. The company is moving from the “we have servers that can be liquid cooled” message, to “we can participate in rack-level AI infrastructure design.” The EIA air-cooled architecture, by contrast, is designed for more conventional data center environments. MSI says its 19-inch, 48RU EIA air-cooled rack supports standard deployments and can be configured with 16 2U2N multi-node systems, with AMD EPYC 9005 and Intel Xeon 6 platform options. That split matters because the AI infrastructure market is not moving in one uniform direction. Hyperscalers, neoclouds, and AI factories may move aggressively into ORv3, liquid cooling, busbar power, and rack-scale designs. Enterprise data centers, managed service providers, and colocation customers often need to work within existing 19-inch rack footprints and existing facility constraints. MSI wants to supply both markets. The CG681-S6093: MSI’s Flagship Liquid-Cooled AI Server The centerpiece of MSI’s NVIDIA-based AI server announcement is the CG681-S6093, a 6U liquid-cooled AI server based on NVIDIA MGX architecture. MSI says the system supports dual AMD EPYC processors and up to eight NVIDIA RTX PRO 6000 Blackwell Server Edition Liquid Cooled GPUs. It also supports 32 DDR5 DIMMs and NVIDIA ConnectX-8 SuperNICs with up to 8×400Gbps networking. This system is a direct entry into high-density AI inference, HPC, simulation, graphics, video, and physical AI workloads. The server is not positioned only for frontier model training. Instead, MSI appears to be aiming at the expanding middle of the AI infrastructure market: large inference clusters, visual computing, simulation, industrial AI, scientific computing, and agentic AI workloads. The next

BUFFALO, N.Y. — In the race to build AI infrastructure, the industry often focuses on GPUs, power availability, and the massive capital investments reshaping the digital infrastructure landscape. But a walk through Motivair’s manufacturing facility in Buffalo, as provided on the eve of the Motivair-Schneider Electric Global Press Event’s tour of the nearby Terawulf Lake Mariner AI campus, offers a reminder that another critical component of the AI boom is being built one coolant distribution unit at a time. During a recent Data Center Frontier Show podcast recorded at Motivair’s Buffalo headquarters, CEO Rich Whitmore described a reality that is becoming bedrock across the industry: Liquid cooling is now very far from being an emerging technology. It is now a prerequisite for deploying the most advanced AI systems. “You cannot deploy AI servers—at least the cutting-edge AI servers—without liquid cooling,” Whitmore said. That observation may be obvious to infrastructure veterans. Yet it points to a larger shift now underway across the data center ecosystem. As AI workloads drive rack densities beyond the practical limits of air cooling, thermal infrastructure has moved from a supporting role to a primary design consideration. For Whitmore and Motivair, that transition did not begin with ChatGPT. From Supercomputing to Commercial AI Long before AI became the defining growth story of the data center sector, Motivair was developing liquid cooling systems for high-performance computing and supercomputing environments. Whitmore describes today’s AI market as less of a technological revolution than a commercialization of capabilities that have existed for years inside elite computing environments. “We cut our teeth in high-performance computing and supercomputing,” Whitmore explained. “What we’re seeing today as we go into the AI era is really a commercialization of traditional supercomputing.” That experience has positioned Motivair differently than many newer entrants rushing into the liquid cooling

ORLANDO, Fla. — For years, the data center industry optimized individual systems: power distribution, cooling, racks, UPS equipment, and mechanical infrastructure. In the AI era, according to Vertiv Distinguished Engineer and Vice President of Technical Business Development Peter Panfil, that approach is no longer sufficient. Speaking during Wednesday morning’s keynote at the 2026 7×24 Exchange Spring Conference, Panfil presented a vision in which the data center itself becomes a single, tightly orchestrated computing appliance—truly an “AI factory” whose success depends less on standalone components than on the seamless interaction between them. Throughout his presentation, titled “Scale at Speed: How Massively Parallel Compute GPUs Are Revolutionizing Data Center Design,” Panfil repeatedly returned to a single imperative: the AI infrastructure race is increasingly defined by execution velocity. “If you think you’re going big enough, go bigger,” he told attendees. “If you think you’re going fast enough, you’re going to have to go faster.” For an industry gathered under the conference’s overarching theme of future-proofing AI infrastructure, Panfil’s message suggested something subtly different. Rather than trying to predict the future, operators should build systems capable of adapting to it. “I would much rather be future ready,” he said, “than future proof.” Speed Becomes the New Competitive Metric One of the keynote’s recurring themes was that deployment speed has become an economic variable in its own right. Panfil argued that hyperscalers and AI providers increasingly view time-to-capacity as directly tied to business value, making delays in construction or commissioning far more expensive than traditional infrastructure inefficiencies. “The cost of speeding up has real benefits right now,” he observed. That urgency is changing the way facilities are assembled. Rather than coordinating numerous independent contractors and subsystem vendors on-site, Panfil described an emerging model built around highly standardized, factory-produced HAC [hot aisle containment] modules—or “hacks”—that arrive

For much of the AI boom, the industry’s attention has centered on GPUs, power availability, and liquid cooling. But according to Cisco Senior Business Development Manager Robin Olds, another critical constraint is rapidly moving to the forefront: the network itself. Speaking with Data Center Frontier on the show floor at Fiber Connect 2026, Olds argued that AI represents a once-in-a-generation shift comparable to the birth of the commercial internet, fundamentally changing traffic patterns and forcing service providers, data center operators, hyperscalers, and emerging neoclouds to rethink infrastructure design. “It’s really like the internet when it was created,” Olds said. “We’re at another intersection in time where we could really see things happening.” AI Is Rewriting the Bandwidth Equation The most significant change may not be compute density alone but the sustained demand AI places on transport networks. According to Olds, service providers are already seeing AI traffic account for roughly 30% of utilization on backbone infrastructure; a dramatic increase from less than 1% only two years ago. As AI workloads continue to proliferate, those utilization levels are expected to rise further. The next wave of agentic AI could amplify that trend. Unlike consumer chatbots, which generate bursty request patterns, autonomous AI agents continuously interact with applications and external services, creating more persistent traffic flows. “Everything’s about chatbots,” Olds observed. “It’s very spiky—up, down. Agentic AI is going to maintain utilization because now I have agents working on my behalf.” For data center developers, network operators, and cloud providers alike, that implies planning not just for peak demand but for elevated baseline utilization across metro and long-haul infrastructure. Compressing the Network Stack Cisco’s response centers on architectural simplification. Olds highlighted the company’s Agile Services Networking framework, which combines router and optical networking technologies with coherent optics to converge functions that historically

This deal follows the industry trend shown in the Flex/EP² acquisition. One transaction targets engineered control and protection systems; the other targets high-power conductive components and value-added services around them. The clarity here is that data center growth is creating investable demand not just for chips and campuses, but for the industrial companies that make the electrical buildout possible. AZIO AI and EVTV: EV maker to AI infrastructure After multiple announcements of deals between the two companies, and the end of May, they decided a corporate merger was in order. The AZIO AI–EVTV merger turns Envirotech Vehicles Inc., from an electric-vehicle company into a prospective power-backed AI infrastructure and data center platform. The core impact is not just an increase in AI servers; it is the combination of compute hardware, controlled land, behind-the-meter power, fiber, modular deployment, and cooling validation building on the AI data center growth story. AZIO describes the deal as part of EVTV’s strategic transformation into an AI infrastructure and compute platform focused on domestic AI deployment, data center operations, and long-term compute capacity expansion. For data center ambitions, the most important element is power access. AZIO and EVTV say about 11 MW of power capacity has been identified at EVTV’s existing site, with hardware orders placed for an initial 6 MW deployment. They are also discussing long-term rights tied to as much as 500 MW of additional same-site capacity. That matters because AI data center development is increasingly constrained by power availability rather than just real estate or server supply. And with the modular infrastructure the company develops, having that power availability is the key to future site growth. EVTV later said it had expanded its controlled development footprint to more than 548 acres, secured dedicated fiber for current and future operations, and was advancing natural

“Amazon is on the leading edge, but it’s not a secret recipe,” he said. What sets the company apart is scale, execution, facility design, geographic mix, and its aggressive pursuit of energy goals. Others are doing the similar things, if through different avenues: Microsoft is investing in closed-loop cooling systems that dramatically reduce evaporative water loss. Google is heavily focused on reclaimed water and using AI to optimize data centers. Meta has long relied on outside-air cooling. And overall, the industry is moving toward liquid cooling for dense AI deployments, “which changes the water equation again,” said Kimball. One of the big variables is location: Climate influences water efficiency, so where a company builds its infrastructure is as important as its cooling methods. Further, power-consumptive AI changes the discussion, he emphasized; traditional enterprise workloads and dense AI training clusters create very different thermal profiles.