NVIDIA Rack Scale Clusters

A Superchip server is a SoC (system on chip) architecture where the CPU, GPU and memory are combined on a single chip, rather than separate components.

A cluster is a collection of connected server nodes within a rack environment. It is designed from the ground up as a single architecture, with each server node connected to the next, rather than separate servers simply placed in the same rack.

Whereas several connected servers can deliver enhanced performance by acting as a single coherent unit, a cluster is designed from the ground up to remove as many bottlenecks and latencies as possible, so the resulting unified architecture will outperform the equivalent number of connected servers.

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 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 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, 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 and 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