AI Factories - Fully Managed from Scan
AI Factories
Next generation datacentres optimised for intelligence at scale
AI factories are the next stage in the evolution of the datacentre. While existing datacentres store vast amounts of mission-critical corporate or public sector data, AI factories provide competitive advantage by transforming data into actionable real-time insights. Instead of data being the product, within an AI factory intelligence is the product, in the form of tokens. AI factories achieve this by orchestrating AI projects from data preparation, through training, fine-tuning to final inferencing. The latter two stages being particularly important as this is where intelligence is made.
In established AI practice, an organisation starts with its own data, develops an AI model, trains that model and uses it to inference data. For example, you feed an AI framework thousands of X-Ray images from an image library and train it to detect a particular cancer. This is then deployed in hospitals to detect that cancer in patient X-Rays.
In contrast, an AI factory starts with a pre-trained foundation model and applies a feedback loop known as a data flywheel to fine-tune the AI model and extract additional intelligence with each iteration. For example, an AI factory-empowered vision language model (VLM) will generate better images, and audio or written interpretation, every time it goes through the data flywheel than the static model.
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.
The AI factory infrastructure can be summarised by NVIDIA's five layer cake concept where each layer — Energy → Chips → Infrastructure → Models → Applications — all influence and rely on each other to create an ecosystem of parts delivering real-time intelligence at scale.
The building blocks of an AI factory can start as small as four multi-GPU server nodes, but may scale to thousands and thousands of racks occupying entire datacentres. The NVIDIA Enterprise AI Factory Validated Design is a blueprint for building a datacentre environment tailored to the demands of hyperscale AI and rapid token generation. Let’s explore the elements further.
GPU-Accelerated Servers
The processing power required for real-time intelligence at scale can only be delivered by powerful systems such as NVIDIA DGX, HGX or Superchip servers equipped with large amounts of GPU or coherent system memory to run the largest and most complex agentic and physical AI models.
These systems employ NVLink and NVSwitch inter-GPU connects for the fastest throughput and minimum latency, crucial for handling large-scale AI workloads.
Infrastructure
Such high-performance GPU compute requires the consistent feeding of large datasets, only possible with AI-optimised storage and low-latency networking to ensure GPU utilisation is maintained.
NVIDIA Spectrum Ethernet switches, Quantum InfiniBand switches and ConnectX SuperNICs and DPUs ensure the server-storage connection is as fast as possible, enabling speeds up to 1,600 GB/s.
Software Stack
The hardware in an AI factory-scale deployment is only half the story, with NVIDIA AI Enterprise software providing all the necessary frameworks and libraries to accelerate your model development.
Additional software layers such as NVIDIA Run:ai cluster management software ensure GPUs are virtualised into a common pool for maximum utilisation, whereas NVIDIA Slurm provides ease of orchestration in containerised environments.
Professional Services
The design and deployment of an AI factory is no mean feat. Choosing whether to host on-premise or in the cloud and in which geography are all key decisions, let alone the actual installation, configuration, and ongoing maintenance and management.
Our datacentre partners, in the UK and across Europe, offer green, sustainable options for AI factories' power and cooling, and our range of professional services provide data science assistance, compliance, security and management of your AI systems.
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].
Partnering with Scan to design, deploy, and service your AI factory ensures seamless integration of cutting-edge hardware with leading industry expertise. Our certified engineers and specialists meticulously design AI factories tailored specifically to your vision for utilising compute, from training large scale LLMs to running vast and complex CFD simulations. Our aim is to execute the deployments of your infrastructure at pace, making the most of your investment into a fast-moving hardware lifecycle.
Frequently Asked Questions
AI factories are the next stage in the evolution of the datacentre. While existing datacentres store vast amounts of mission-critical corporate or public sector data, AI factories provide competitive advantage by transforming data into actionable real-time insights.
Instead of data being the product, in AI factories intelligence is the product. AI factories achieve this by orchestrating AI projects from data preparation, through training, fine-tuning and inferencing. The latter two stages being particularly important as this is where intelligence is made.
An AI factory is a specialised, high-performance computing environment that functions like a manufacturing plant, taking raw data as input and producing intelligent insights, models, and real-time predictions as output. Unlike a general-purpose datacentre, an AI factory is optimised to continuously train and deploy AI models, transforming data into actionable intelligence at scale.
Any organisation that wants to scale its use of artificial intelligence efficiently and responsibly can benefit from an AI factory. This includes:
- 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 provide numerous benefits that help businesses mature their AI capabilities:
- Faster time-to-value with automated data preparation, training, and deployment to reduce lead times
- Scalability, with the ability to reuse pipelines and models across teams and use cases
- Consistency, applying standardised workflows, reducing error and technical debt
- Improved ROI, streamlining operational costs and increasing model performance
- Governance and compliance with added centralised control over data usage, lineage, and versioning
- Adaptability, integrate with cloud, hybrid, or on-premise environments to match infrastructure needs
Yes, small businesses can use AI factories by leveraging cloud platforms that provide pre-built templates and low-code options. These tools make it easier and more affordable for smaller teams to deploy AI workflows without building complex infrastructure from scratch. Contact our Scan Cloud team for your readiness check today.
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 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, 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