AI Transformation Starts with Distributed Infrastructure

TL:DR

• High-performance infrastructure supports distributed data sources & provides compute, edge processing & interconnection that traditional data centers can't match.
• Vendor-neutral platforms ensure multicloud flexibility, optimize GPU performance & maintain data sovereignty with private interconnection.
• Equinix's 270+ AI-ready data centers in 76 markets offer global reach & 10,000+ partners for AI ecosystems.

Over the past several years, enterprise leaders have seen glimpses of what's possible with advanced technologies like large language models and agentic AI. If you're one of them, you're likely planning how to accelerate AI adoption. Today's emerging AI use cases aren't just an evolution of this technology-what we're seeing is revolutionary. It's reshaping how decisions are made, how products are built and how businesses compete.

AI is also redefining what infrastructure needs to do. Compared to traditional IT, AI workloads demand significantly more power, lower latency and better ecosystem access. You won't meet those requirements with a few upgrades to existing data centers. And a single-cloud approach will only lead to vendor lock-in, compromised performance and high egress fees.

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AI infrastructure needs to support data sources that are increasingly distributed. According to Gartner®:

"By 2028, more than 50% of enterprise-managed data will be created and processed outside the data center or cloud, which is a major increase from 25% in 2024."[1]

To ensure your AI workloads can run wherever they perform best, you'll need distributed infrastructure that includes:

• High-performance data centers distributed globally with advanced compute capacity for training workloads
• Edge infrastructure close to data sources for low-latency inference
• Interconnection capabilities to move distributed data quickly and securely

Your ability to connect with a diverse ecosystem of partners will be critical to accessing the capabilities and services you need to drive your AI strategy forward.

It may feel daunting to face such a transformative moment in business history, but the scope of this opportunity is also very exciting. Let's look closer at some of the factors you'll want to consider to capitalize on this AI opportunity.

Infrastructure neutrality enables data and workload mobility

To optimize distributed infrastructure for AI, you need to deploy on a neutral infrastructure platform that removes the risk of vendor lock-in and puts you in control. That freedom to choose from different partners ensures you're able to create the right mix of technology for your AI strategy. For instance, you can access AI infrastructure and services from multiple cloud providers with the help of flexible, scalable multicloud networking. This is very important in an era when multicloud is becoming the norm. According to IDC:

"By 2028, more than 90% of newly developed applications will be multicloud enabled, having been architected to leverage platform-delivered capabilities and deliver more innovative solutions."[2]

On a neutral infrastructure foundation, you can choose both old and new partners:

• If you have vendors that you already know and trust, such as traditional hardware providers or clouds, you won't have to give up those existing relationships or abandon any previously deployed data or workloads.
• To help fill the holes in your technology portfolio and unlock new capabilities, you can then pair those existing partnerships with emerging GPU as a Service (GPUaaS) providers, specialized SaaS providers and neoclouds.

No matter which partners you choose, you'll be able to stitch together services in different places to form a holistic, interconnected foundation for AI. Your data will move quickly between different locations to enable distributed AI workloads. Also, you'll be able to future-proof your AI operations. As the AI landscape changes and new providers and models emerge, your infrastructure can evolve with it.

A vendor-neutral colocation provider like Equinix offers the platform that distributed AI infrastructure requires, bringing together thousands of partners and customers side by side in the data center. And when that data center is part of an interconnected global platform, it allows you to connect your AI infrastructure throughout the world while also connecting with partners wherever they may be located.

Data privacy doesn't mean compromising on performance

In addition to optimizing flexibility, a neutral infrastructure platform puts you in a better position to meet your data privacy and sovereignty requirements. You can expect to face various regulatory requirements in different countries as you scale your AI strategy across the globe-and data privacy is a non-negotiable you can't afford to compromise.

According to Gartner, "Current data governance practices are often too rigid and insensitive to the business context. By 2027, for example, 60% of organizations will fail to realize the anticipated value of their AI use cases due to incohesive data governance frameworks."[3] But with the right approach to distributed infrastructure, you can maintain control over where your data is processed and how it's governed. This includes setting up private infrastructure in specific locations to meet data sovereignty requirements and ensuring certain datasets remain within prescribed borders.

When your strategy calls for cloud services, cloud on-ramps let you move data on your terms. That means transferring only select datasets for specific purposes, while maintaining copies of those datasets on private infrastructure. This helps ensure your data stays under your control and only goes where you say it should go.

Cloud on-ramps use dedicated, private interconnection, allowing you to avoid the public internet and its inherent privacy issues. While some private encryption capabilities can degrade performance, interconnection solutions like Equinix Fabric® offer privacy without compromise. They provide private connectivity for distributed AI with better performance than the public internet.

Privacy without compromise is essential because many businesses are investing millions in GPUs, and AI-ready hardware can only be as good as the networking that connects it. According to Gartner, "Since 2023, GPUs have dominated the training and development of AI models. Their revenue is projected to total $51 billion, an increase of 27%, in 2025."[4] It's essential that companies don't let the investments they've already made go to waste. Network latency can prevent GPUs from performing at their best. When network traffic causes delays, GPUs have to wait for data to arrive before they can process it, leading to low utilization rates. Optimizing their networks can help businesses make the most of the GPUs they already have. This is one reason that interconnection is the backbone of AI-ready data centers.

Share insights with an expansive partner ecosystem

The universe of AI ecosystem partners is expanding fast. It includes a wide range of infrastructure service providers and AI data and model providers. Exchanging data with partners requires low-latency connectivity and strict data privacy. Both these factors are essential to enabling advanced use cases like federated AI.

With federated AI, partners train their own models locally and then aggregate the model weights to form a shared global model. For instance, airlines can collect insights based on telemetry data from different airplane components and share them with one another. This helps them better understand what makes airplane components fail, when they're likely to fail and how to be proactive about avoiding it. They're able to do this without sharing their raw datasets. This dramatically reduces the amount of data each partner has to move. Moving less data keeps performance high and costs low.

Since all the partners need to connect to the model aggregation point without putting themselves at risk, it's best to host shared infrastructure on a neutral platform. Also, hosting federated AI in places where there's already a robust partner ecosystem helps ensure low latency, while interconnection services can help partners connect with each other quickly and securely. On all these accounts, Equinix data centers fit the bill.

Start deploying distributed AI infrastructure today

The future of AI is distributed. The time for transformation is now. And Equinix provides the foundation on which this future is being built.

The Equinix ecosystem includes more than 10,000 enterprises and service providers. No matter who you want to connect with to drive your AI strategy forward-be it a business partner that's sharing data or a service provider that's enabling scalable, flexible infrastructure-there's a very good chance you'll find them at Equinix.

Only Equinix offers the global reach that distributed AI demands. We have 270+ AI-ready data centers across 76 strategic markets worldwide. Since 90% of all internet users worldwide are within 10 milliseconds of an Equinix connection, we're able to support real-time AI applications where the public internet falls short.

In addition, you'll be able to connect with partners on demand in locations around the world using Equinix Fabric, our Network as a Service solution. As your AI strategy continues to evolve, you'll be able to stand up new distributed infrastructure wherever the need arises and set virtual connections to ensure the flow of data and workloads between those locations.

Read the Equinix Indicator to learn what industry experts are saying about distributed AI infrastructure and how enterprises can start deploying it today.

[1] Gartner, Modernize File Storage Data Services with Hybrid Cloud, by Julia Palmer and Vishesh Divya, September 2024.

[2] IDC, IDC FutureScape: Worldwide Cloud 2025 Predictions, #US52640724, October 2024.

[3] Gartner Insights, Enhance Your Roadmap for Data and Analytics Governance, 2025.

[4] Gartner Press Release, Gartner Forecasts Worldwide Semiconductor Revenue to Grow 14% in 2025, October 28, 2024.

GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.

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Jon Lin Chief Business Officer

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