AI Factories - Fully Managed from Scan
AI Factories
Next generation datacentres optimised for intelligence at scale
AI factories are the next step for the datacentre. A normal datacentre stores data. An AI factory turns that data into real-time insight, in the form of tokens. It does this by managing every stage of an AI project: preparing data, training models, fine-tuning them, and running inference. Fine-tuning and inference matter most, because that's where the intelligence gets made.
In traditional AI, you start with your own data, build a model, train it, then use it to make predictions. For example: feed a model thousands of X-ray images and train it to spot a specific cancer. Hospitals can then use it to detect that cancer in patient scans.
An AI factory works differently. It starts with a pre-trained foundation model, then fine-tunes it using a feedback loop called a data flywheel. Each time the model runs through this loop, it gets a bit better. A vision language model (VLM) running in an AI factory will produce sharper images and better written or spoken interpretation with every cycle, something a static model simply can't do.
The faster the AI factory can process tokens, and the more it learns per iteration of the flywheel, the faster the AI agent can respond to customer queries — both simple and complex — and the more relevant these responses become.
NVIDIA sums up AI factory infrastructure as a "five layer cake": Energy → Chips → Infrastructure → Models → Applications. Each layer depends on the one below it, working together to deliver real-time intelligence at scale.
An AI factory can start small, as few as four multi-GPU server nodes, or scale up to thousands of racks across entire datacentres. NVIDIA's Enterprise AI Factory Validated Design gives a blueprint for building this kind of environment, built for hyperscale AI and fast token generation. Here's a closer look at each element.
GPU-Accelerated Servers
Real-time intelligence at scale needs serious processing power. That means systems like NVIDIA DGX, HGX or Superchip servers, built with large amounts of GPU or system memory to run the biggest, most complex AI models.
They connect GPUs directly using NVLink and NVSwitch, giving the fast throughput and low latency that large AI workloads need.
Infrastructure
High-performance GPUs need a constant supply of data. That means AI-optimised storage and low-latency networking, to keep your GPUs fully utilised. NVIDIA Spectrum Ethernet, Quantum InfiniBand switches, and ConnectX SuperNICs and DPUs keep the server-to-storage connection as fast as possible, up to 1,600 GB/s.
Software Stack
Hardware is only half the story. NVIDIA AI Enterprise software gives you the frameworks and libraries to speed up model development.
On top of that, NVIDIA Run:ai pools your GPUs together for maximum use, and NVIDIA Slurm makes it easy to manage containerised environments.
Professional Services
Designing and deploying an AI factory isn't simple. You'll need to decide whether to host on-premise or in the cloud, and where, on top of installation, configuration, and ongoing maintenance.
Our datacentre partners across the UK and Europe offer green, sustainable power and cooling. Our professional services team can help with data science, compliance, security, and ongoing management.
As well as these standalone building blocks, NVIDIA also has a number of reference architecture SuperPOD solutions combining optimised superchip server, storage and networking into custom rack environments for immediate deployment.
Build with Confidence
Scan is the UK's leading NVIDIA Elite partner and the only certified NVIDIA DGX Managed Services Provider (MSP). To discuss your AI projects or challenges, don't hesitate to contact our experts on 01204 474747 or at [email protected].
When you partner with Scan to design, deploy and service your AI factory, you get cutting-edge hardware backed by real expertise. Our certified engineers design AI factories around what you actually need, whether that's training large language models or running complex simulations. And we move fast, so you get the most from your investment before the hardware moves on.
Frequently Asked Questions
AI factories are the next step for the datacentre. A normal datacentre stores data. An AI factory turns that data into real-time insight, it's intelligence, not data, that comes out the other end.
It does this by managing every stage of an AI project: data preparation, training, fine-tuning, and inference. Fine-tuning and inference matter most, because that's where the intelligence gets made.
Think of an AI factory as a manufacturing plant for intelligence. Raw data goes in. Insights, trained models and real-time predictions come out. Unlike a general-purpose datacentre, it's built to continuously train and deploy AI models, turning data into useful intelligence at scale.
Any organisation that wants to scale AI use efficiently and responsibly can benefit from an AI factory, including:
- Enterprises managing large datasets and multiple AI projects
- Healthcare providers using AI for diagnostics or patient management
- Financial institutions deploying AI for fraud detection or risk modelling
- Retailers and e-commerce companies focused on personalisation and demand forecasting
- Manufacturers aiming to automate processes or predict maintenance
- Government and research bodies working with sensitive or high-volume data
AI factories offer several benefits that help businesses mature their AI use:
- Faster results — automated data prep, training and deployment cut lead times
- Scalability — reuse pipelines and models across teams and use cases
- Consistency — standardised workflows reduce errors and technical debt
- Better ROI — lower operational costs, stronger model performance
- Governance and compliance — centralised control over data usage, lineage and versioning
- Adaptability — works with cloud, hybrid or on-premise environments
Yes. Small businesses can use cloud platforms with pre-built templates and low-code tools, making it easier and more affordable to deploy AI workflows without building complex infrastructure from scratch. Contact our Scan Cloud team for a readiness check.
An AI factory helps your business scale AI by systematising the model development pipeline, allowing teams to reuse components, reduce development time, ensure consistency, and deploy models faster. This scalability is essential for companies looking to integrate AI into multiple departments or products.
Yes. AI factories can support real-time AI inference and batch processing simultaneously, using intelligent workload management to balance low-latency applications with large-scale AI workloads while maximising GPU utilisation and performance
Yes, AI factories can integrate across environments. Hybrid and multi-cloud setups allow companies to process data where it lives — whether in the cloud, at the edge (e.g., IoT devices), or in secure on-premises systems — always ensuring flexibility and compliance. Contact our AI team for your readiness check today.
Yes, as a leading NVIDIA Elite partner and the UK's only certified NVIDIA DGX Managed Services Provider (MSP), Scan sells all the individual elements to create your own AI factory. This includes NVIDIA DGX, NVIDIA Run:ai software licences. We also offer a range of professional services for ease of deployment and management.
Migrating to an AI factory model is a strategic move that requires planning, the right tools, and experienced support. Scan acts as your specialist partner to streamline this transformation, bringing technical expertise, cutting-edge infrastructure, and industry-proven workflows.
Here's how the migration journey typically unfolds with Scan by your side:
- Discovery & assessment — starting with a full audit across the whole business that identifies bottlenecks in data prep, model development, and deployment
- AI factory blueprint design — our teams work with you to design a scalable and secure AI factory architecture tailored to your goals, including standardised data ingestion pipelines, reusable model training and deployment workflows, governance and monitoring frameworks, and on-premise, cloud, or hybrid infrastructure integration
- Infrastructure & platform setup — choose infrastructure and tools that support scalability, are built for performance, security and long-term growth
- Automation and MLOps integration — we help operationalise your AI workflows with best-in-class MLOps practices, introducing version control, CI/CD pipelines, model monitoring, and retraining strategies to enable continuous delivery of AI models across teams and business units
- Team enablement — Scan not only builds the factory, we empower your team to run it. Alongside NVIDIA, we can provide documentation, training, and ongoing support to ensure your data scientists, engineers, and decision-makers can maintain, scale, and improve the system confidently
- Ongoing optimisation & support — your AI factory isn't a static solution. Scan offers long-term support and optimisation services, helping you manage compute resources efficiently, track carbon usage, refine models, and adopt emerging AI tools