How To Get Started Right With Distributed AI

Before investing in AI infrastructure, it helps to pause and look at how the balance of data, compute and intelligence is shifting. In working with companies just starting their AI journey, we’ve seen clear patterns in what separates early success from later struggle. The location of data, the pace of expansion to the edge and the diversity of models and partners all shape what “doing AI right” really means. It starts with building a foundation for distributed AI.

Edge use cases are on a meteoric rise. As models become smaller, smarter and more domain-specific, inference is increasingly happening closer to where data is created, whether that’s fraud detection at a transaction point, real-time quality control on a factory floor or decision-making inside connected vehicles. Agentic AI architectures and emerging frameworks like the Model Context Protocol (MCP) are accelerating this trend, enabling modular AI systems that live and run across distributed environments.

The result: roughly 75% of new data is expected to be generated and processed at the edge.[1] At the same time, the ecosystem is rallying around platforms and blueprints from leaders like NVIDIA, providing reference architectures that help enterprises scale distributed AI faster. There are other equally-important components of a distributed AI infrastructure solution, that together, help businesses do AI right.

Data fuels the AI engine

AI begins and ends with data. Today, about 60% of enterprise data still resides within the major public clouds[2], but the newest and most valuable data is being created outside them, at the edge. This imbalance has created what many describe as a data tug-of-war: Data is constantly moving in and out of clouds and to and from edge locations as organizations balance where it’s stored, processed and analyzed. These flows now dictate how and where AI infrastructure must evolve. In many cases, it’s the movement of data, not the workloads themselves, that drives the next generation of infrastructure decisions.

What’s driving the need for distributed AI infrastructure?

Supporting this distributed reality takes more than GPUs. It requires an infrastructure capable of moving massive datasets quickly, securely and intelligently across clouds, centralized infrastructure and edges.

AI workloads vary dramatically, from data preparation and model training to natural language processing, generative AI and computer vision. Each comes with distinct demands for compute, storage and connectivity. Training requires massive, parallel compute and high-throughput, low-latency interconnects to move data efficiently between nodes. Inference, on the other hand, depends on ultra-fast response times and proximity to users and data sources.

No single, purpose-built environment can excel at both ends of that spectrum. Scale and speed must coexist, which means the underlying infrastructure has to flex across different workloads and adapt dynamically as needs change.

Some teams are putting smaller, domain-specific models at the edge, while others keep a feedback loop running between inference and training to continuously refine models in near real time. Whatever the approach, success depends on effective automation and coordination across the system. With so many moving parts in play, establishing the right foundation is essential—one that can adapt to dynamic data flows, scale globally and operate as a unified, intelligent system.

Interconnection is the connective tissue

No matter where an organization starts its AI journey, success ultimately depends on how well everything connects. AI is inherently distributed: Data in one place, compute in another, models and inference engines spread across regions and clouds. Each element may be optimized on its own. Still, without the ability to move data, synchronize models and orchestrate workflows seamlessly and securely across environments, the system will always be constrained.

And that’s where many AI strategies converge on the need for intelligent, real-time connectivity. What makes distributed AI work is how easily data, models and insights can move between environments. When that flow is fast and secure, the network stops being background infrastructure and becomes the engine of coordination and scale.

Enabling the seamless flow of data intelligence

Interconnection is the connective tissue of AI infrastructure. It enables the flow of intelligence between the places where data is generated, where it is processed and where insights are acted upon. It brings coherence to the growing sprawl of AI-ready infrastructure, linking GPUs in private or public clouds to datasets in on-premises environments, connecting distributed inference endpoints back to centralized training sites and bridging partner ecosystems that span multiple providers. Without interconnection, AI cannot operate as a unified system.

Creating a dynamic, global multicloud network

Building this connective fabric requires a global multicloud network that is purpose-built for AI. One that offers low-latency reach, secure and predictable data paths and the flexibility to adapt to changing workloads in real time. As enterprises move from experimentation to scaled deployment, the network must evolve from static connections to dynamic, intelligent interconnections capable of learning and optimizing continuously.

Enabling the next generation of interconnection

This is the vision behind Fabric Intelligence—the next generation of interconnection for the AI era. It brings new, AI-ready capabilities that make it easier for enterprises to link into the right services and providers, across clouds, partners and edge sites. Built into the network itself, it can surface available AI resources, suggest smarter connection paths based on performance or cost, and adjust those links automatically as needs shift. And it lays the groundwork for a full-stack observability strategy, ensuring that the entire AI fabric, compute, data and network operate transparently and efficiently.

Together, these capabilities form the backbone of a global inference network. One that secures, simplifies and accelerates the movement of intelligence wherever it needs to flow.

Getting started: The blueprint era is here

AI is moving closer to where data is generated and consumed across all industries and verticals, enabled by smaller, smarter LLMs. Real-time decisions depend on low-latency and the ability to move data quickly and intelligently between sources. Blueprints are the new vehicle for building consistent, repeatable workflows for all manner of AI, and MCP enables flexibility and choice. Whether building on NVIDIA platforms or designing your Agentic AI template with technology partners, blueprints are enabling faster time-to-market.

Equinix Distributed AI™

Businesses need distributed AI infrastructure located everywhere their data lives to drive decision-making securely, and with speed, flexibility and ease. As the next wave of AI innovation unfolds, integrating a real-time interconnection strategy with agents and blueprints will lead to success.

Equinix Distributed AI™ is a comprehensive solution that helps businesses deploy the infrastructure they need to accelerate AI innovation at scale wherever opportunities exist. They can build a foundation on neutral infrastructure that can help them meet evolving requirements without being constrained by vendor limitations or inflexible technology choices. The extensive, intelligent connectivity built into Equinix Distributed AI™ helps businesses move data and inference closer to users while meeting data privacy requirements.

Equinix offers the global reach that supports distributed AI with our 270+ AI-ready data centers across 77 strategic markets worldwide. The Equinix ecosystem includes more than 10,000 enterprises and service providers, from established cloud providers to emerging AI specialists. Since many of the service providers you need to connect with are at Equinix, it’s easy to find the right partners for evolving your distributed AI strategy. Simply interconnect with them at one of our Equinix IBX® colocation data centers to get started exchanging data quickly and securely.

Learn why the future of AI infrastructure is distributed: View videos with Equinix executives for an introduction to our Distributed AI solution.

[1] Bruce Kornfeld, “2025 IT Infrastructure Trends: The Edge Computing, HCI And AI Boom,” Forbes, December 12, 2024.

[2] Soundarya Jayaraman, “150+ Fascinating Cloud Computing Statistics for 2025,” G2.ai, December 23, 2024.