Many edge devices can periodically send summarized or selected inference output data back to a central system for model retraining or refinement. That feedback loop helps the model improve over time while still keeping most decisions local. And to run efficiently on constrained edge hardware, the AI model is often pre-processed by techniques such as quantization (which reduces precision), pruning (which removes redundant parameters), or knowledge distillation (which trains a smaller model to mimic a larger one). These optimizations reduce the model’s memory, compute, and power demands so it can run more easily on an edge device. What technologies make edge AI possible? The concept of the “edge” always assumes that edge devices are less computationally powerful than data centers and cloud platforms. While that remains true, overall improvements in computational hardware have made today’s edge devices much more capable than those designed just a few years ago. In fact, a whole host of technological developments have come together to make edge AI a reality. Specialized hardware acceleration. Edge devices now ship with dedicated AI-accelerators (NPUs, TPUs, GPU cores) and system-on-chip units tailored for on-device inference. For example, companies like Arm have integrated AI-acceleration libraries into standard frameworks so models can run efficiently on Arm-based CPUs. Connectivity and data architecture. Edge AI often depends on durable, low-latency links (e.g., 5G, WiFi 6, LPWAN) and architectures that move compute closer to data. Merging edge nodes, gateways, and local servers means less reliance on distant clouds. And technologies like Kubernetes can provide a consistent management plane from the data center to remote locations. Deployment, orchestration, and model lifecycle tooling. Edge AI deployments must support model-update delivery, device and fleet monitoring, versioning, rollback and secure inference — especially when orchestrated across hundreds or thousands of locations. VMware, for instance, is offering traffic management

Our first, conservative, view says that AI’s network impact is largely confined to the data center, to connect clusters of GPU servers and the data they use as they crunch large language models. It’s all “horizontal” traffic; one TikTok challenge would generate way more traffic in the wide area. WAN costs won’t rise for you as an enterprise, and if you’re a carrier you won’t be carrying much new, so you don’t have much service revenue upside. If you don’t host AI on premises, you can pretty much dismiss its impact on your network. Contrast that with the radical metaverse view, our third view. Metaverses and AR/VR transform AI missions, and network services, from transaction processing to event processing, because the real world is a bunch of events pushing on you. They also let you visualize the way that process control models (digital twins) relate to the real world, which is critical if the processes you’re modeling involve human workers who rely on their visual sense. Could it be that the reason Meta is willing to spend on AI, is that the most credible application of AI, and the most impactful for networks, is the metaverse concept? In any event, this model of AI, by driving the users’ experiences and activities directly, demands significant edge connectivity, so you could expect it to have a major impact on network requirements. In fact, just dipping your toes into a metaverse could require a major up-front network upgrade. Networks carry traffic. Traffic is messages. More messages, more traffic, more infrastructure, more service revenue…you get the picture. Door number one, to the AI giant future, leads to nothing much in terms of messages. Door number three, metaverses and AR/VR, leads to a message, traffic, and network revolution. I’ll bet that most enterprises would doubt

Microsoft’s second Fairwater data center in Atlanta isn’t just “another big GPU shed.” It represents the other half of a deliberate architectural experiment: proving that two massive AI campuses, separated by roughly 700 miles, can operate as one coherent, distributed supercomputer. The Atlanta installation is the latest expression of Microsoft’s AI-first data center design: purpose-built for training and serving frontier models rather than supporting mixed cloud workloads. It links directly to the original Fairwater campus in Wisconsin, as well as to earlier generations of Azure AI supercomputers, through a dedicated AI WAN backbone that Microsoft describes as the foundation of a “planet-scale AI superfactory.” Inside a Fairwater Site: Preparing for Multi-Site Distribution Efficient multi-site training only works if each individual site behaves as a clean, well-structured unit. Microsoft’s intra-site design is deliberately simplified so that cross-site coordination has a predictable abstraction boundary—essential for treating multiple campuses as one distributed AI system. Each Fairwater installation presents itself as a single, flat, high-regularity cluster: Up to 72 NVIDIA Blackwell GPUs per rack, using GB200 NVL72 rack-scale systems. NVLink provides the ultra-low-latency, high-bandwidth scale-up fabric within the rack, while the Spectrum-X Ethernet stack handles scale-out. Each rack delivers roughly 1.8 TB/s of GPU-to-GPU bandwidth and exposes a multi-terabyte pooled memory space addressable via NVLink—critical for large-model sharding, activation checkpointing, and parallelism strategies. Racks feed into a two-tier Ethernet scale-out network offering 800 Gbps GPU-to-GPU connectivity with very low hop counts, engineered to scale to hundreds of thousands of GPUs without encountering the classic port-count and topology constraints of traditional Clos fabrics. Microsoft confirms that the fabric relies heavily on: SONiC-based switching and a broad commodity Ethernet ecosystem to avoid vendor lock-in and accelerate architectural iteration. Custom network optimizations, such as packet trimming, packet spray, high-frequency telemetry, and advanced congestion-control mechanisms, to prevent collective

Land & Expand is Data Center Frontier’s periodic roundup of notable North American data center development activity, tracking the newest sites, land plays, retrofits, and hyperscale campus expansions shaping the industry’s build cycle. October delivered a steady cadence of announcements, with several megascale projects advancing from concept to commitment. The month was defined by continued momentum in OpenAI and Oracle’s Stargate initiative (now spanning multiple U.S. regions) as well as major new investments from Google, Meta, DataBank, and emerging AI cloud players accelerating high-density reuse strategies. The result is a clearer picture of how the next wave of AI-first infrastructure is taking shape across the country. Google Begins $4B West Memphis Hyperscale Buildout Google formally broke ground on its $4 billion hyperscale campus in West Memphis, Arkansas, marking the company’s first data center in the state and the anchor for a new Mid-South operational hub. The project spans just over 1,000 acres, with initial site preparation and utility coordination already underway. Google and Entergy Arkansas confirmed a 600 MW solar generation partnership, structured to add dedicated renewable supply to the regional grid. As part of the launch, Google announced a $25 million Energy Impact Fund for local community affordability programs and energy-resilience improvements—an unusually early community-benefit commitment for a first-phase hyperscale project. Cooling specifics have not yet been made public. Water sourcing—whether reclaimed, potable, or hybrid seasonal mode—remains under review, as the company finalizes environmental permits. Public filings reference a large-scale onsite water treatment facility, similar to Google’s deployments in The Dalles and Council Bluffs. Local governance documents show that prior to the October announcement, West Memphis approved a 30-year PILOT via Groot LLC (Google’s land assembly entity), with early filings referencing a typical placeholder of ~50 direct jobs. At launch, officials emphasized hundreds of full-time operations roles and thousands

As the global data center industry races through its most frenetic build cycle in history, one question continues to define the market’s mood: is this the peak of an AI-fueled supercycle, or the beginning of a structurally different era for digital infrastructure? For Green Street Managing Director and Head of Global Data Center and Tower Research David Guarino, the answer—based firmly on observable fundamentals—is increasingly clear. Demand remains blisteringly strong. Capital appetite is deepening. And the very definition of a “data center market” is shifting beneath the industry’s feet. In a wide-ranging discussion with Data Center Frontier, Guarino outlined why data centers continue to stand out in the commercial real estate landscape, how AI is reshaping underwriting and development models, why behind-the-meter power is quietly reorganizing the U.S. map, and what Green Street sees ahead for rents, REITs, and the next wave of hyperscale expansion. A ‘Safe’ Asset in an Uncertain CRE Landscape Among institutional investors, the post-COVID era was the moment data centers stepped decisively out of “niche” territory. Guarino notes that pandemic-era reliance on digital services crystallized a structural recognition: data centers deliver stable, predictable cash flows, anchored by the highest-credit tenants in global real estate. Hyperscalers today dominate new leasing and routinely sign 15-year (or longer) contracts, a duration largely unmatched across CRE categories. When compared with one-year apartment leases, five-year office leases, or mall anchor terms, the stability story becomes plain. “These are AAA-caliber companies signing the longest leases in the sector’s history,” Guarino said. “From a real estate point of view, that combination of tenant quality and lease duration continues to position the asset class as uniquely durable.” And development returns remain exceptional. Even without assuming endless AI growth, the math works: strong demand, rising rents, and high-credit tenants create unusually predictable performance relative to

In a coordinated leadership transition this fall, Ryan Mallory has stepped into the role of CEO at Flexential, succeeding Chris Downie. The move, described as thoughtful and planned, signals not a shift in direction, but a reinforcement of the company’s core strategy, with a sharpened focus on the unprecedented opportunities presented by the artificial intelligence revolution. In an exclusive interview on the Data Center Frontier Show Podcast, Mallory outlined a confident vision for Flexential, positioning the company at the critical intersection of enterprise IT and next-generation AI infrastructure. “Flexential will continue to focus on being an industry and market leader in wholesale, multi-tenant, and interconnection capabilities,” Mallory stated, affirming the company’s foundational strengths. His central thesis is that the AI infrastructure boom is not a monolithic wave, but a multi-stage evolution where Flexential’s model is uniquely suited for the emerging “inference edge.” The AI Build Cycle: A Three-Act Play Mallory frames the AI infrastructure market as a three-stage process, each lasting roughly four years. We are currently at the tail end of Stage 1, which began with the ChatGPT explosion three years ago. This phase, characterized by a frantic rush for capacity, has led to elongated lead times for critical infrastructure like generators, switchgear, and GPUs. The capacity from this initial build-out is expected to come online between late 2025 and late 2026. Stage 2, beginning around 2026 and stretching to 2030, will see the next wave of builds, with significant capacity hitting the market in 2028-2029. “This stage will reveal the viability of AI and actual consumption models,” Mallory notes, adding that air-cooled infrastructure will still dominate during this period. Stage 3, looking ahead to the early 2030s, will focus on long-term scale, mirroring the evolution of the public cloud. For Mallory, the enduring nature of this build cycle—contrasted