What Is an AI Factory?

Enterprise leaders recognize the promise of generative and agentic AI, but they don’t always understand how to get the infrastructure they need to enable these technologies. They may understand that it involves deploying high-performance computing inside AI-ready data centers that help the hardware run at its full potential. But they may not know how to acquire the right hardware or find the data center capacity they need.  

For that matter, many of these leaders have already been looking to get out of the data center business. They’re tired of the high CAPEX costs and complexity involved with running a data center, and they understand that their time and efforts would be better spent elsewhere. They’d prefer to focus on how AI can support their business strategy and leave the nuts and bolts of building AI infrastructure to someone else. This is why an AI factory solution can be helpful.  

How can an AI factory help? 

Deploying an AI factory solution can help organizations get the infrastructure and services they need to quickly unlock impactful AI use cases across industry verticals. For instance: 

• In financial services, big banks need a combination of high-performance GPU clusters and infrastructure at the edge to enable AI agents that perform complex tasks such as real-time risk assessment and proactive fraud detection. They also need distributed infrastructure and private connectivity to keep up with stringent data sovereignty and privacy requirements in different jurisdictions. 

• In telecom, agentic AI will lead to a new era of network automation. This means that agents will detect poor performance or potential outages and then take action to address the issue, without the need for direct human intervention. However, these agents can’t optimize network performance without visibility into real-time network performance. This means that telcos need to ensure AI agents are properly connected to observability tools, wherever they’re hosted. 

• In public sector, government agencies with special privacy and security concerns can quickly access the hardware they need to train their own private models, as well as the data center capacity to keep that hardware running at full potential. They can also connect with partners and peer agencies while ensuring their data is never exposed to hostile entities. 

In short, no matter what leaders hope to achieve with AI, an AI factory solution helps to achieve it faster with a well-packaged model, limiting complexity and reducing risk. 

What are the components and characteristics of an AI factory? 

There’s good reason that we call this solution a “factory.” Just like a traditional factory, an AI factory turns raw materials—in this case, data—into a finished product (new business applications).  

It’s also important to remember that a factory is merely one important step in the wider manufacturing process. It requires an interconnected supply chain of partners working to extract and refine the raw materials, as well as transportation infrastructure to get it to the processing site.  

This is also true when it comes to AI factories. That’s because AI isn’t one thing that happens in one place. It typically requires distributed infrastructure in many different places and environments, because the data and workloads are inherently distributed. For instance, training workloads and inference workloads often run in different places because they have different infrastructure requirements. Enterprises need to be able to support these different workloads, while also ensuring the connectivity to move data between them quickly. 

Training workloads require processing muscle 

Starting with the public launch of ChatGPT in late 2022, enterprises were generally focused on their training workloads for the first several years of the generative AI era. Leaders were curious about the possibilities of AI, but they knew they needed to get the right models before they could execute their AI strategy.  

Whether they trained their own models or fine-tuned models acquired from a provider, folks needed powerful AI processing hardware. This hardware typically came in the form of GPUs, but other AI accelerators such as LPUs, NPUs and TPUs were also important. When enterprises acquired this hardware, they couldn’t deploy it just anywhere. It needed to run it inside AI-ready data centers that provided reliable power, advanced cooling capabilities and operational practices required for highly reliable environments to achieve their intended results. 

Inference workloads require low latency 

In 2026, model training is still important, but organizations are increasingly turning their attention to how they can perform real-time inference at scale. These inference workloads must be hosted in proximity to data sources to ensure low latency. Otherwise, performing inference based on outdated information would inevitably lead to inaccuracies and poor results. 

The reason an AI factory is so valuable to enterprises is that it provides a single, consolidated solution for all the distributed components of their AI strategy. It includes the powerful hardware they need to do training, but also the edge infrastructure they need to get close to data sources and ensure low-latency inference.  

In addition, an AI factory includes the networking technology they need to move data seamlessly between different AI endpoints. This networking not only connects an enterprise’s own distributed AI infrastructure, but it also connects them to AI ecosystem partners such as model providers, data brokers and neoclouds. 

As we’ll explore below, Nanyang Biologics deploying our joint solution with NVIDIA is a great example of an AI factory in action. 

Equinix and NVIDIA help enterprises accelerate AI  

Last year, we announced the Equinix AI Factory accelerated by NVIDIA. This solution brought together cutting-edge NVIDIA DGX GB300 and DGX B300 systems and NVIDIA Mission Control software with Equinix’s global reach, AI-ready data centers, interconnection solutions and dense partner ecosystem. This solution makes it quicker and easier for enterprises to go from exploring AI to production AI use cases with measurable impact.   

Nanyang Biologics, a Singapore-based biotech company, used technology from Equinix, NVIDIA and HPE to harness the power of AI for its drug discovery work. 

The company uses AI to analyze molecules derived from natural compounds and discover their effects on the human body. This could help unlock the therapeutic potential of those compounds. With the joint solution from Equinix and partners, the company was able to speed drug discovery by 68% at a fraction of the cost.  

Equinix helps enterprises prepare for an AI-driven future  

Enterprise leaders understand the opportunities agentic AI presents, but getting the digital infrastructure needed to capture them isn’t always straightforward. The organizations that move quickly to establish the right infrastructure foundation will be the ones that define the next era of their industries, while those that delay this important step will risk ceding ground to more agile competitors. 

The good news is that enterprises no longer need to figure this out alone. AI factory solutions remove the guesswork and help address the risk, cost and complexity of deploying AI infrastructure. Instead of stitching together hardware, data centers, networking and managed services from disparate vendors, leaders can focus on what really matters: building the AI-powered products, services and experiences that will define what their business becomes next. 

Equinix provides the digital infrastructure foundation that makes AI factories work on a global scale. The Equinix Distributed AI™ framework brings together diverse ecosystem partners in different locations throughout the world, enabling enterprises to support modern AI workloads and data that are inherently distributed. Our global portfolio of colocation data centers offers access to AI-ready capabilities such as advanced liquid cooling, and interconnection solutions like Equinix Fabric® help our customers connect with their partner ecosystem quickly. 

It’s because of these capabilities that customers like Nanyang Biologics can accelerate their AI strategies and connect with all the right partners to support those strategies. Read the full Nanyang Biologics story to learn more.