What’s an AI Factory, and How Is It Different from a Data Center?

The term “AI factory” has rapidly moved from niche academic jargon to a cornerstone of the technology industry’s vocabulary. While often used interchangeably with “data center,” the AI factory represents a fundamental shift in how we think about computing. It is no longer just a place to store data; it is a high-speed production line for intelligence. The term was popularized by NVIDIA to describe their Enterprise AI Factory, a full-stack, validated design that includes NVIDIA’s GPUs, networking, and AI Enterprise software. Emphasis on AI inference performance and energy efficiency. With these, NVIDIA says enterprises can build AI factories ready for agentic AI, physical AI, and high-performance computing workloads.

However, AI factories were probably originally defined by two Harvard Business School professors, Marco Iansiti and Karim Lakhani, in their 2020 book, “Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World.” They defined AI factories as,“The scalable decision engine that powers the digital operating model of the twenty-first-century firm.”

Since then, industry leaders have refined this definition to reflect the physical and digital reality of the technology:

“An AI factory is a digital infrastructure designed to develop, train, and deploy artificial intelligence (AI) models at scale.”
—Supermicro

An AI factory is a purpose-built environment that enables enterprises to industrialize artificial intelligence, accelerating time-to-value by transforming data into actionable insights at the speed and scale required to thrive in today’s complex, data-driven world.”
—HPE

“AI Factories are dynamic ecosystems that will build around AI-optimised supercomputers, offering computing resources and support services…”
—European High Performance Computing Joint Undertaking (EuroHPC-JU)

What Goes into an AI Factory?

Most experts agree that an AI factory is a purpose-built ecosystem that consists of hardware and software designed to manage the entire AI lifecycle. Basically, AI factories must produce intelligent predictions through processes that run on specialized hardware and are automated by software.

This requires a seamless integration of several core functions:

• Data pipeline management: cleaning, transforming, storing data
• Model building and training
• Deployment and inference
• Continuous governance and optimization
• Automation of all the above

Real-World Applications

AI factories are already operational across several sectors around the world, including:

• Healthcare: The Mayo Clinic utilizes these environments for digital pathology, processing vast amounts of imaging data to assist in diagnoses.
• Finance: Institutions use AI factories for real-time fraud detection and risk assessment, identifying anomalies in millions of transactions per second.
• Public Infrastructure: The European High Performance Computing Joint Undertaking (EuroHPC-JU) is building dynamic ecosystems around AI-optimized supercomputers to support regional innovation.

Where Can AI Factories Be Deployed?

In the excitement over chips and algorithms, the physical building is often overlooked. What kind of facilities and infrastructure are needed to house and run this specialized computing equipment? Factories don’t exist without the buildings that enable the equipment to operate. AI is no different. They require special infrastructure, such as high floor loading capacity, tall ceilings, high-capacity electrical systems, access to ample power and fiber lines, environmental controls, designated areas for production lines, water supply, back-up systems, security, and fiber. The efficiency of an AI factory is not only about the hardware or software, it depends on the infrastructure of the building itself.

• High-density power: AI chips consume significantly more electricity than standard CPUs, requiring specialized electrical distribution.
• Advanced cooling: The thermal load from high-performance computing is immense. Modern AI factories are increasingly moving toward liquid cooling systems to manage the heat.
• Structural integrity: High floor-loading capacity is necessary to support the weight of dense server racks.
• Connectivity: Massive fiber-optic throughput is required to move data between the “production lines.”

The Shift to Liquid Cooling

Water is about 25 times more thermally conductive than air, and it is more than 3,000 times more effective by volume at capturing and moving heat. That’s why most AI factories are being built to support liquid-to-chip infrastructure.

There are two primary methods currently being integrated into these physical structures:

• Direct-to-Chip (cold plates): Liquid circulates through a metal plate sitting directly on the GPU or CPU. This targets the “hot spots” specifically, allowing the rest of the facility to operate with less intensive ambient cooling.
• Immersion Cooling: Entire server blades are submerged in a thermally conductive, non-conductive (dielectric) liquid. This eliminates the need for fans entirely, drastically reducing the noise and energy overhead of the building.

So in essence, AI factories are data centers that house specialized hardware and provide high-density power and cooling and ultra-low latency connectivity.

Do AI Factories Require New Construction?

In many cases, no. RAEDEN specializes in converting already existing structures, such as former industrial buildings, laboratories, and other commercial sites with the necessary infrastructure to be modern AI data centers or AI factories. This approach is often better for AI due to the availability of already existing sites in urban centers near user bases where there is diverse, low-latency connectivity. Our technical commercial real estate team finds buildings with anywhere from 2MW to 50MW+ power capacity that meet the necessary infrastructure requirements. Then, our data center solutions and operations teams convert them to AI-ready facilities faster than most new data centers can be built. The only case where new construction is often needed is for multi-gigawatt facilities.

Adapting existing structures to be modern data centers is often faster, more cost-effective, and more sustainable than building new ones. If your company is in need of space for your AI factory, no matter how small, reach out to us at sales@raeden.com to start exploring your options.