AI is helping humanity tackle all kinds of real-world problems, from fraud detection to customer service optimization to healthcare research and treatment. It’s revolutionizing the diagnosis and treatment of diseases like cancer by improving cancer detection, drug development and personalization of care, while accelerating medical research.
IT infrastructure is at the heart of this work. For instance, AI requires significant compute power to analyze huge volumes of data from across numerous hospital systems and healthcare institutions. The many participants in the healthcare ecosystem need ways to securely integrate and exchange all this data. And compliance with regulatory and privacy requirements for sensitive health data is crucial.
Many organizations across industries are facing similar challenges with AI. As the AI boom continues and organizations explore new possibilities for deploying AI solutions that drive business value, they’re realizing the need for scalable and resilient AI infrastructure that can prepare them for an unpredictable future. Legacy on-premises data centers weren’t designed for AI and typically aren’t equipped to meet its demands. As a result, enterprises and service providers alike are examining the potential of high-performance data centers to help them take the next step in their AI journey.
To build a strong foundation for AI, it’s crucial to be in the right type of data center for your AI workloads—whether you’re training an AI model, deploying it for inference at the edge or supporting the movement of data throughout the AI lifecycle. Hyperscale, colocation and edge data centers all play important roles, depending on your specific requirements. An AI-ready data center provides the infrastructure to support high-density, power-intensive AI workloads, including high-performance networking equipment for connectivity to clouds, service providers and the rest of your AI ecosystem.
Networking, however, is an often-overlooked element of AI solutions. AI requires massive amounts of data to be transferred to and from applications quickly. You can have the best AI hardware on the market, but to use it effectively, you must be able to securely transfer data between environments at speed. Interconnection—the direct, private exchange of data between businesses—is therefore a critical enabler of AI success.
Why interconnection matters for AI
Interconnection can address many of the problems organizations are trying to solve around AI data exchange:
- Reducing upfront capital investments
- Lowering cloud egress fees
- Increasing performance of AI solutions
- Deploying AI applications quickly
- Providing observability into the AI network
Almost all organizations are now operating in a hybrid multicloud architecture, where data is generated and stored across dispersed locations. Having effective connectivity across these complex, global architectures is pivotal for AI initiatives given the volumes of distributed data needed for AI.
As AI ecosystems and tools grow, the role of partners has become even more vital. No one can succeed with AI alone, and there are significant advantages to be gained from partnerships. Interconnection enables secure, private data exchange between partners.
For many companies, this data exchange that’s so fundamental to AI includes at least some proprietary or sensitive data. When enterprises acquire an AI model from a public cloud or AI model marketplace, for instance, they often need to use proprietary data to make the model more relevant to their business. Thus, more organizations are exploring private AI architectures to address this need. While you can use the public internet for AI networking, for many enterprises, it doesn’t offer the security, privacy and control they need over their data, nor the consistent performance their AI solutions demand. Private interconnection, on the other hand, allows you to exchange data securely and provides the reliable, consistent performance needed for a private AI project.
Breaking down AI’s interconnection requirements
Networking requirements differ for each type of AI workload, and interconnection can play a role in meeting these connectivity requirements securely and cost-effectively.
AI model training
If you’re developing a model, you need to pull in data from many sources to train it, including clouds, private storage and data marketplaces. Transferring large datasets requires significant bandwidth as well as secure connections between those data sources and the training environment. For training, you also need a global network because the needed data is likely located all around the world.
A globally available interconnection service connects leading service providers for secure, private data exchange to accelerate AI initiatives. Virtual interconnection solutions offer the agility to easily scale up and down when you need it—so you’re not investing in networking infrastructure that you won’t need later.
AI inference
When you deploy a model for inference, data flows continuously into and out of the AI model. Queries come in from users to your inference model and then go back out to the user. This requires real-time access to the latest data, and the data exchange needs to happen with ultra-low latency to ensure application performance.
A global interconnection solution delivers real-time data access where you need it, at the edge, with reliable performance. Doing AI inference in edge data centers offers many advantages. It helps avoid the inefficiency and cost of transferring data back and forth to a central location for processing. Doing AI inference at the edge also reduces round-trip latency and gives you access to local data markets. And it’s worth noting that the emergence of agentic AI will further increase the need for low-latency, high-availability connectivity for inference.
For every phase of an AI workflow, you’re dealing with data in motion between clouds, data centers, AI marketplaces, AI services providers and partner ecosystems. Interconnection is the best way to transfer data privately and securely, with optimized performance for each type of AI workload.
Build an AI interconnection strategy on a global platform
As collaborative AI initiatives take off, the important role of interconnection in AI-ready data centers is becoming even more obvious. Secure, private, high-bandwidth connectivity is the backbone of AI, and working with experienced interconnection providers can reduce the complexity while ensuring access to your AI ecosystem.
AI-ready data centers are designed to accelerate AI deployment, reduce costs, increase efficiency and future-proof your AI infrastructure. And secure, high-speed connectivity is a key element of that AI-readiness.
