Inside the AI factory: where data becomes intelligence

AI has moved from experimentation to everyday use.

But as models become more capable, organisations are hitting a familiar wall: they can't train, tune or deploy them fast enough. The reason? Infrastructure built for earlier generations of technology.

AI factories are changing that. They represent a new type of digital infrastructure, designed specifically to create and refine AI at scale.

To unpack what they are and why they matter, we sat down with Jim Leach, Chief Technology Officer at Nexus Core Systems, who leads the design of NCS's technology platforms and partnerships with NVIDIA and other global providers.

Q: Let's start simple – what exactly is an AI factory?

At the highest level, an AI factory is where data becomes intelligence.

Traditional data centres were built to store and move information; AI factories are built to create it. They manage the full life cycle – ingesting data, training and tuning models, and serving them at scale.

You can think of it as a production line for intelligence. The output isn't files or storage, it's decisions, predictions and automation. And that's a big shift in how we think about infrastructure.

Q: For someone developing AI models, how does that actually help them? When should they start thinking about infrastructure?

Usually, too late. Many teams begin in the cloud or on local GPUs, but as soon as they start scaling beyond proofs of concept, they hit limitations – costs rise, performance drops and data governance becomes complex.

An AI factory gives them a path to keep scaling. It's optimised for continuous training and high-volume inference, so you can train faster, deploy sooner and control where your data and models live.

If you're building or finetuning models regularly – or moving from R&D into production – that's the point you should be making an infrastructure decision. It's about building a reliable, high-performance environment for your AI.

Q: What makes AI environments so different from traditional data centres or the cloud?

It comes down to purpose.

Traditional data centres were built to store and move data; AI environments are built to process and transform it… billions of times a second.

AI workloads are far denser, hotter and more latency sensitive than anything a general purpose cloud was designed to handle. Instead of spreading tasks across many CPUs, AI factories use GPUs working in parallel as a single system. That's what makes large scale model training and inference possible in practical timeframes.

Where the cloud focuses on elasticity (spinning capacity up and down), AI factories focus on consistent, predictable performance that lets teams control how and where their models run.

At NCS, we're designing AI factories around NVIDIA's accelerated computing stack because it's the most mature, energy efficient and scalable foundation available. But it's not just about raw processing power, it's about the full ecosystem: networking, cooling, orchestration and software integration, all tuned to maximise efficiency.

Q: What makes AI factories more efficient than general purpose infrastructure?

Efficiency comes from it's single purpose. In an AI factory, every component – from GPU architecture to power and cooling – is engineered for one task: running AI.

That end-to-end design delivers far higher compute density and energy efficiency than legacy systems.

GPUs lie at the heart of that performance. They can process workloads in parallel, often delivering up to twenty times the output of CPUs for the same power draw. When the entire environment is optimised around that principle, the benefits compound.

We measure efficiency by how much intelligence each watt of energy produces. Direct-to-chip liquid cooling and renewable integration drive those numbers further, cutting both energy and water use.

The impact is shorter training times, faster inference and significantly lower energy consumption per model. Sustainability comes as the result of designing systems that run more efficiently.

Q: How would you summarise the defining characteristics of an AI factory?

There are four.

• Acceleration – every layer, from the GPU to the network switch, is optimised for parallel processing.
• Integration – compute, cooling and orchestration act as one system.
• Efficiency – the goal is the most intelligence per watt, per rack, per square metre.
• Scalability – modular by design, so you can expand as demand grows.

The best factories are composable: they let you easily re-allocate resources between training, inference and simulation.

Q: Who are AI factories built for?

We're designing AI factories for four main groups, each facing different challenges but the same constraint: access to reliable, scalable compute.

• AI Pioneers and Model Builders are creating the next generation of models; researchers, start-ups, AI-first teams. They don't want to fight for GPU time or worry about scaling.
• Enterprises Driving AI Innovation are moving beyond experimentation and need predictable capacity, compliance and performance.
• Research and Education Institutions rely on stable, sovereign environments to run data intensive workloads and accelerate discovery.
• Governments and Sovereign Programmes view compute as national infrastructure. They need secure, compliant capacity that keeps data within borders and underpins public services and innovation ecosystems.

Each group starts from a different place, but they all need the same thing: access to sustainable, high performance capacity built for AI, not adapted for it.

Q: How can organisations actually access AI factories? Do they have to build their own?

That's the beauty of it, they don't. We're building the environments so teams can tap into them however they need.

• GPU-as-a-Service – Some prefer this, which gives them on-demand access to accelerated compute capacity. This is ideal for model developers, AI start-ups or research institutions that need burst capacity or flexible scaling.
• Bare metal – Others choose this, where they can run their own workloads on dedicated infrastructure for maximum performance and control.
• AI optimised colocation – And then there's this, which suits organisations that already own their hardware and simply need to host it within a purpose built, sovereign AI environment.

It's the same foundation underneath – GPU-dense, liquid-cooled, high-efficiency – but the delivery model adapts to where each organisation is on its AI journey.

Q: Looking ahead, how do you see AI factory design evolving?

We're entering a phase where models are becoming more capable; able to reason, plan and make decisions. That kind of workload demands continuous inference at a completely different scale.

Future AI factories will be increasingly software defined, using intelligent orchestration to balance power, cooling and workload in real time. They'll be adaptive systems, learning how to run themselves more efficiently, almost like digital ecosystems.

What won't change is the principle: purpose built environments will always outperform retrofitted ones.

Q: What advice would you give to teams thinking about where to run their AI?

Start with your workload, not your hardware. Map where your compute demand is today and where it's heading. Then decide what level of control, scalability and sovereignty you need.

The earlier you make that call, the smoother your path to production will be. The right infrastructure doesn't just support AI, it enables it.

Closing remarks

AI factories are becoming the backbone of modern computing. For NCS, the mission is clear: to build the infrastructure that lets innovators train faster, deploy sooner and scale responsibly.