How Nebius Is Building a Globally Scalable AI Infrastructure Platform
The race for AI dominance isn't just about who has the most GPUs; it’s about where those GPUs live and how fast you can connect to them. While many providers struggle with the logistical hurdles of global expansion, Nebius is rewriting the playbook. By leveraging a distributed architecture and private, high-performance connectivity via Equinix, they’ve deployed massive GPU clusters globally in a matter of months rather than years. This isn’t just a win for speed; it’s a massive unlock for enterprise-grade reliability. In our latest video, we dive into how this distributed model provides a critical "cloud on-ramp" that balances raw power with regional control. You’ll see how Nebius is leveraging dense interconnection to offer early access to high-demand hardware, such as NVIDIA H200 GPUs, in Europe, while ensuring sensitive data remains compliant with local sovereignty regulations.
Interconnection Redefined
Interconnection is the invisible engine of the digital economy, enabling the exchange of information between systems, clouds, networks, partners, and users. At its core, interconnection means connecting two or more parties together so data can move between them. Yet the term is too often used only to describe private, direct links that bypass the public internet. While private connectivity is essential and the preferred option for business-critical traffic, this limited view doesn't reflect how enterprises operate in a highly distributed, cloud-first world. Today’s digital infrastructure spans multiple public clouds, SaaS platforms, partner ecosystems, and edge locations. Users connect from anywhere. In this environment, no single connectivity model meets every need. Some workloads demand tightly controlled, predictable performance. Others require broad reach and flexible access. In practice, interconnection encompasses both private and public connectivity working together, with each applied where it delivers the most value. The Right Mix Private interconnection forms the core of modern enterprise connectivity. Physical and virtual connections provide consistent performance, stronger security, and greater control for sensitive and mission-critical applications. At the same time, public connectivity plays an important supporting role, enabling scale, reach, and accessibility for use cases such as user access, content delivery, and SaaS consumption. The most effective interconnection approaches are private-first, with public connectivity used selectively to extend reach without compromising control. Equinix supports this model by bringing together enterprises, clouds, carriers, SaaS providers, and partners in close proximity across its global platform. Rather than forcing customers into a single connectivity approach, Equinix offers a comprehensive portfolio of interconnection services within the same locations. Customers can establish private cross-connects and virtual connections for high-performance, secure traffic, while also accessing rich carrier ecosystems, internet exchanges, and public connectivity options when needed. Because these capabilities exist side by side, organizations gain flexibility without added complexity. Location Matters This proximity delivers meaningful business outcomes that go beyond simple connectivity. When systems, clouds, and partners are located in the same facility, traffic travels shorter physical distances, reducing latency, jitter, and points of failure. Applications respond faster and perform more consistently, which directly improves user experience and service reliability. Shorter paths also reduce dependency on long-haul transit, lowering costs and minimizing exposure to congestion or outages outside your control. At the same time, having a dense ecosystem in one place accelerates execution. Teams can connect to new providers, deploy services, or enter new markets in days rather than months because the infrastructure and partners are already there. The result is a network that is not only faster and more secure, but also easier to scale and adapt as business needs evolve. The Equinix Edge Interconnection becomes a competitive advantage when choice and proximity come together at scale. This is where the concept moves beyond simple links between systems and transforms into something far more powerful. With private and public connectivity options available side by side in the same locations, businesses gain immediate control over how every workload connects, performs, and scales. They can keep critical traffic on secure, high-performance paths, extend outward to the public internet for reach, and shift between both instantly as needs change. Few providers can deliver this level of optionality across a global footprint. By combining dense ecosystems with a full portfolio of interconnection services, Equinix turns connectivity into capability, giving customers the freedom, speed, and resilience to design digital infrastructure that actively drives business growth rather than merely supporting it.
How Merck KGaA, Darmstadt, Germany Is Leveraging AI to Advance Scientific Discovery
Imagine accelerating the journey from raw data to life-changing discovery—that's exactly what's happening with the new high-performance data center from Merck KGaA, Darmstadt, Germany, built in collaboration with Lenovo and hosted in one of Equinix's AI-ready data centers. This powerful new platform is empowering Merck's teams to leverage Artificial Intelligence (AI) for scientific discovery and advanced analytics more effectively, ultimately accelerating the pace of innovation. FAQs: Q: What is high-performance computing (HPC)? A: High-performance computing explained: HPC refers to the use of supercomputers and parallel processing techniques to solve complex computational problems. In this video, we explore how Merck KGaA is leveraging HPC to accelerate innovation. Q: How do liquid cooling systems improve HPC performance? A: Liquid cooling systems efficiently remove heat at the source, enabling high-performance workloads to run without compromising speed or energy efficiency. Q: Why is hybrid cloud infrastructure important for innovation? A: Hybrid cloud infrastructure combines private and public cloud infrastructure, offering flexibility, scalability, and cost optimization — key factors for driving scientific breakthroughs and how AI is transforming innovation and discovery.
Beyond the Hype: The CIO's Guide to Eliminating the Hidden Costs and Complexity of AI at Scale
The promise of Artificial Intelligence (AI) is clear: groundbreaking efficiency, new revenue streams, and a decisive competitive edge. But for you, the IT leader, the reality often looks a little different. You’ve moved past the initial proofs of concept. Now, as you attempt to scale AI across your global enterprise, the conversation shifts from innovation to infrastructure friction. You’re hitting walls built from unpredictable data egress fees, daunting data residency mandates, and the sheer, exhausting complexity of unifying multicloud, on-prem, and edge environments. The network that was fine for basic cloud adoption is now a liability—a bottleneck that drains budget and slows down the very models designed to accelerate your business. I'm Ted, and as an Equinix Expert and Global Principal Technologist here at Equinix, I speak with IT leaders every day who are grappling with these exact challenges. They want to know: What are the hidden costs when training AI across multiple clouds? How do we keep AI training data legally compliant across countries and regions? How can I balance on-prem, cloud, and edge when running AI workloads without adding more complexity? How to predict and control network spend when running apps across multiple clouds? What’s the best way to ensure my AI workloads don’t go down if one cloud region fails? The short answer is: You need to stop viewing your network as a collection of static, siloed pipes. You need a unified digital infrastructure that eliminates complexity, centralizes control, and makes compliance a feature, not a frantic afterthought. In this deep-dive, we'll unpack the major FAQs of scaling enterprise AI and show you how a platform-centric approach—leveraging the power of Equinix Fabric and Network Edge—can turn your network from an AI impediment into a powerful, elastic enabler of your global strategy. Ready to architect your way to AI success? Let's get started. Q: What are the hidden costs when training AI across multiple clouds? A. The AI landscape is inherently dynamic, with dominant players frequently being surpassed by innovative approaches. This constant evolution necessitates a multicloud strategy that provides flexibility to adopt new technologies and capabilities as they emerge. Organizations must be able to pivot quickly to leverage advancements in AI models, tools, and cloud services without being constrained by rigid infrastructure or high migration costs. However, the rub is, as cloud AI training scales, network-related costs often become the most unpredictable part of the total budget. The main drivers are data egress fees, inefficient routing, and duplicated network infrastructure. Data egress charges grow rapidly when moving petabytes of training data between clouds or regions, especially when traffic traverses the public internet. Unoptimized paths add latency that extends training cycles, while replicating firewalls, load balancers, and SD-WAN devices in every environment creates CapEx-heavy, operationally complex networks. Security infrastructure for network traffic is often duplicated between clouds, leading to cost inefficiencies. The solution lies in re-architecting data movement around private, software-defined interconnection. By replacing internet-based transit with direct, high-bandwidth links between cloud providers, organizations can reduce egress costs, improve throughput, and maintain predictable performance. Deploying virtual network functions (VNFs) in proximity to cloud regions also lowers hardware spend and simplifies management. Beyond addressing hidden cost, this approach gives IT leaders the agility to scale up or down with AI demand. As GPU clusters spin up, bandwidth can be turned up in minutes; when cycles finish, it can scale back just as fast. This elasticity avoids stranded investments while ensuring compliance and security controls remain consistent across clouds and regions. By unifying connectivity and network services on a single digital platform, Equinix helps enterprises eliminate hidden costs, accelerate data movement, and ensure the network is a strategic enabler rather than a bottleneck for AI adoption. Specifically, Equinix Fabric helps customers create private, high-performance connections directly between major cloud providers, enabling data to move securely and predictably without traversing the public internet. By extending this flexibility, Equinix Network Edge allows VNFs such as firewalls, SD-WAN, or load balancers to be deployed as software services near data sources or compute regions. Together, these capabilities form a unified interconnection layer that reduces hidden network costs, accelerates training performance, and simplifies scaling across clouds. Q: How do we keep AI training data legally compliant across countries and regions? A. Data sovereignty and privacy regulations increasingly shape how and where organizations can process AI data. Frameworks such as GDPR and regional residency laws often require that sensitive datasets remain within geographic boundaries while still being accessible for model training and inference. Balancing those requirements with the need for scalable compute across clouds is one of the core architectural challenges in enterprise AI. To address this, many enterprises choose to keep data out of the cloud but near it, placing it in neutral, high-performance locations adjacent to major cloud on-ramps. This approach enables control over where data physically resides while still allowing high-speed, low-latency access to any cloud for processing. It also helps avoid unnecessary egress fees, since data moves into the cloud for analysis or training but not back out again. By establishing deterministic, auditable connections between environments leveraging private, software-defined interconnection keeps data flows under enterprise control, rather than relying on public internet paths. As a result, organizations can enforce consistent encryption, access control, and monitoring across regions while maintaining compliance. This also translates into greater control and auditability of data flows. Workloads can be positioned in compliant locations while still accessing global AI services, GPU clouds, and data partners through secure, private pathways. By combining governance with agility, Equinix makes it possible to pursue your most pressing global AI strategies while still reducing risk. Today, Equinix Fabric can support this approach by enabling private connectivity between enterprise sites, cloud regions, and ecosystem partners, helping data remain local while workloads scale globally. Equinix Network Edge complements this by allowing in-region deployment of virtualized security and networking functions, so policies can be enforced consistently without requiring physical infrastructure in every jurisdiction. Together, these capabilities offer customers a foundation for compliant, globally distributed AI architectures. As a result, customers can create network architectures that not only reduce compliance risk but also turn regulatory constraints into a competitive advantage by delivering trusted, legally compliant AI services, based on the right data at the right time in the right place at global scale. Q: How can I balance on-prem, cloud, and edge when running AI workloads without adding more complexity? A. Determining where AI workloads should run involves balancing control, performance, and scalability. On-premises environments offer data governance and compliance, public clouds deliver elasticity and access to advanced AI tools, and edge locations provide low-latency close to users and devices. Without a unified strategy, this mix can lead to fragmented systems, inconsistent security, and rising operational complexity. One effective approach is a hybrid multicloud architecture that standardizes connectivity and governance across all environments. Equinix defines hybrid multicloud architecture as a flexible and cost-effective infrastructure that combines the best aspects of public and private clouds to optimize performance, capabilities, cost, and agility. This design allows workloads to move seamlessly between on-prem, cloud, and edge based on performance, regulatory, or cost needs without rearchitecting each time. As a result, organizations can employ a hybrid multicloud architecture where policies, security, and connectivity are consistent across all environments. AI training can happen in the cloud with high-bandwidth interconnects, inference can run at the edge with low-latency access to devices, and sensitive datasets can remain on-premises to maintain regulatory compliance. This architecture enables seamless interconnection across clouds, users, and ecosystems, supporting evolving business needs.If customers utilize Network Edge VNFs they can access a control plane to manage traffic flows seamlessly across these environments, ensuring workloads are placed where they deliver the most business value with a predictable cost. It also enables the deployment of virtual network functions such as firewalls, load balancers, and SD-WAN as software services, reducing hardware overhead and improving consistency. Together, they create a common network fabric that simplifies operations, supports workload mobility, and maintains governance across diverse environments. As a result, customers can minimize complexity by centralizing management, turning what used to be a fragmented sprawl into a unified, agile, and compliant AI operating model. Q: How to predict and control network spend when running apps across multiple clouds? A. As AI and multicloud workloads scale, network costs often become the least predictable element of total spend. Massive east-west data movement between training clusters, storage systems, and clouds can trigger unexpected egress and transit fees, while variable routing across the public internet adds latency and complicates cost forecasting. These factors can make it difficult for IT and finance teams to align budgets with actual workload behavior. A more sustainable approach is to build predictability and efficiency into the interconnection layer. By replacing public internet paths with dedicated, software-defined connections, organizations can achieve elastic bandwidth scaling while having predictable billing. This model not only ensures stable and reliable network performance but also enhances cost transparency, enabling businesses to optimize their connectivity expenses while supporting evolving operational demands. Equinix Fabric supports this model by enabling private, high-performance connections to multiple clouds and ecosystem partners from a single port, fostering predictability in network performance. Equinix Network Edge complements this by allowing network functions such as firewalls, SD-WAN, and load balancers to be deployed virtually, reducing CapEx and aligning spend with actual utilization. Together, they deliver a unified network architecture that stabilizes performance, enhances cost transparency, and enables organizations to scale bandwidth effectively while managing costs in alignment with their AI and multicloud workloads. Q: What’s the best way to ensure my AI workloads don’t go down if one cloud region fails? A. AI workloads are highly distributed, and regional outages can disrupt training, inference, or data synchronization across clouds. Relying on a single provider or static internet-based paths introduces latency and failure risks that can cascade across operations. Building resilience into the interconnection layer ensures continuity even when one region or cloud becomes unavailable. The key is to design for multi-region redundancy with pre-established, high-performance failover paths. By maintaining secondary connections across clouds and geographies, organizations can automatically reroute workloads and traffic without interruption or loss of performance. Equinix Fabric enables this design by providing software-defined, private connectivity to multiple cloud providers and regions. Equinix Network Edge complements it by supporting virtualized global load balancers, SD-WAN, and firewalls that dynamically redirect traffic and enforce security policies during failover. Together, they create a resilient, globally consistent architecture that maintains availability and performance even when individual cloud regions experience disruption.Innovation Meets Sustainability: Louis Vuitton
In this video, Daniel Roux - CTO of Louis Vuitton tells us the shared mission of Louis Vuitton and Equinix to support sustainability goals through advanced digital solutions, including AI-driven innovations and 100% clean and renewable energy coverage. Discover how LV Neo, the tech arm of Louis Vuitton, is leveraging Equinix’s IT solutions to reduce its carbon footprint while maintaining reliability, security and innovation. This partnership demonstrates how luxury brands can lead the way in adopting technology solutions committed to sustainability without compromising on performance or security. Learn more about Equinix's Future First commitment to sustainability
NetworkChuck: This is where Internet Lives
Imagine billions of dollars of tech, AI GPUs & LPUs, and the literal backbone of the internet, all humming along in one of the most secure buildings on Earth. We're talking about the secret sauce that makes the digital world go 'round, and NetworkChuck's taking us on an exclusive tour of Equinix DA11 & The Infomart! Be sure to share the LinkedIn post from Equinix!
Maximize Your AI Investment with Equinix and HPE
Check out this Tech Talk with experts from Equinix and HPE Greenlake as they dive into their game-changing partnership. They'll show you how their private cloud solutions, powered by HPE GreenLake and NVIDIA, are designed for serious scalability, security, and speed. You'll learn how to get seamless data integration, low-latency connectivity, and rapid time-to-value for all your enterprise AI workloads. Deploy scalable and secure AI workloads quickly
