AI factories · NVIDIA data centre · UK
An AI factory turns your data, power and GPUs into a steady output of answers, predictions and agents. We design, build, install and run NVIDIA AI factories for UK organisations, from a single rack to a multi-rack scalable unit.
A traditional data centre stores and serves information. An AI factory produces it. Raw material goes in (your documents, records, sensor data and code), and finished goods come out: answers to staff questions, decisions made by agents, forecasts, generated designs and trained models. The unit of output is the token, and the job of the factory is to make good tokens quickly, cheaply and safely.
Like any factory, it is more than its machines. GPUs do the work, but the throughput you actually get depends on the network that connects them, the storage that feeds them, the software that schedules them, the power and cooling that keep them running, and the people who operate the whole line. Most AI projects that disappoint do so because one of those links was undersized, not because the GPUs were too slow.
That is why we treat an AI factory as one engineered system with one owner. We size it from the work you need done, check that your building can host it, build and test it in the UK, and keep it running once it is live.
Why organisations are moving serious AI work off rented GPUs and onto infrastructure they control
Cloud GPUs are billed by the hour whether the work is valuable or not. Owned capacity is a fixed cost, so every extra query, agent run or training job lowers the cost of each one. Once utilisation is steady, the maths usually favours owning.
Models, prompts, embeddings and outputs never leave infrastructure you control. That makes UK GDPR, sector regulators and client contracts far simpler, and it keeps sensitive work away from shared multi-tenant platforms.
No quota requests, no waiting for a region to free up, no surprise price changes. Your teams plan around capacity you already own, and urgent work goes first because you set the priorities.
The same cluster can pre-train, fine-tune, serve models, run retrieval over your documents and host agents. Workload scheduling shares GPUs between teams, so expensive hardware is busy instead of sitting idle in one department.
Each NVIDIA generation produces far more tokens per unit of power than the last. NVIDIA quotes up to 50 times more output per megawatt for Blackwell Ultra systems than Hopper on low-latency agentic work. With UK electricity prices, efficiency is a real budget line.
You choose the identity provider, the network boundary, the logging and whether the factory is connected to the internet at all. Air-gapped and restricted-network designs are possible when the data demands it.
Start with one node or one rack built to a reference design, then add identical racks as demand proves itself. Because each step follows the same blueprint, growth does not mean redesign.
Fine-tuned models, evaluation sets, prompt libraries and agent workflows become assets you own. Every project you run on the factory makes the next one faster, rather than paying a provider to relearn your business.
Owning is not right for everyone. If your GPU demand is occasional or unpredictable, or you need capacity for a few weeks only, cloud or a small personal AI system is usually cheaper. We will tell you if that is the case.
GPU servers or rack-scale systems sized to the models you run: memory per GPU for model size, NVLink domain size for how far one model can spread, and precision support (FP4 to FP64) for the maths you need.
The GPU-to-GPU network between servers, built on NVIDIA Quantum InfiniBand or Spectrum-X Ethernet at 400 to 800 Gb/s per GPU. It decides whether 32 GPUs behave like one big machine or 32 small ones.
Parallel or high-throughput storage that keeps GPUs fed during training and checkpointing, plus a separate network for it so storage traffic never competes with GPU traffic.
NVIDIA AI Enterprise, NIM microservices, Kubernetes or Slurm, workload scheduling such as Run:ai, and cluster management such as Mission Control, configured so teams can self-serve safely.
Modern GPU racks draw 30 to over 100 kW each. Power feeds, heat rejection, direct liquid cooling where needed, and floor loading are designed first, not discovered on delivery day.
Monitoring, firmware and driver updates, security patching, model roll-outs and capacity planning. A factory that is not operated well slowly loses its output to failures and drift.
How a typical Workstation AI factory project runs
We start with the work, not the hardware: which models, how many users, how many tokens per day, how much training, and how fast it must grow. That gives a target throughput we can design against.
We check your server room or colocation space for power per rack, cooling capacity, floor loading, access routes and network connectivity. If the site cannot host the design, we say so before you spend anything.
We choose the platform and network, produce a rack layout and bill of materials, and work through allocation and any export-control checks with our suppliers before committing to a firm quote.
Systems are assembled, cabled and stress-tested at rack level, with GPU, memory, network and storage validation, so problems are found in a test bay rather than in your data hall.
We install, connect the fabric, deploy the operating system, drivers, Kubernetes or Slurm and the NVIDIA software stack, and run acceptance tests with your team before handover.
Our SysOps247 service can monitor and maintain the factory around the clock, roll new models out safely with Ring Promoter, and keep secrets and edge access locked down with WSL Vault and WSL Proxy.
A quick guide to the NVIDIA data-centre platforms we build AI factories on. Each has its own page with the detail.
| Platform | GPUs per unit | GPU memory | Cooling | Best at |
|---|---|---|---|---|
| HGX B300 32-GPU rack | 32 (4 × 8) | 8.4 TB HBM3e | Air or liquid | Reasoning inference and large training on standard racks |
| Compal SGX30-2 (HGX B300 node) | 8 | 2.1 TB HBM3e | Air | A certified, ready-specified B300 node, available to order |
| HGX B200 cluster | 8 per node | 1.4 TB per node | Air or liquid | Mixed AI and FP64 scientific computing |
| GB300 NVL72 | 72 in one NVLink domain | 20 TB HBM3e | Liquid | The largest models and fastest reasoning |
| GB200 NVL72 | 72 in one NVLink domain | 13.4 TB HBM3e | Liquid | Trillion-parameter models, plus strong FP64 |
| DGX SuperPOD | Hundreds to thousands | Scales by unit | Liquid or air | A turnkey, NVIDIA-validated AI supercomputer |
| RTX PRO 6000 AI factory | 8 per node | 768 GB per node | Air | Enterprise agents, visual computing and simulation |
| H200 NVL servers | Up to 8 per server | 141 GB per GPU | Air | Inference in existing air-cooled racks |
| Vera Rubin NVL72 | 72 in one NVLink domain | 288 GB HBM4 per GPU | Liquid | Planning the next generation of capacity |
Memory figures are NVIDIA's published totals for each platform. Networking for every option is covered on our AI factory networking page.
Pick a platform to see what it does, what it needs from your site and how we deliver it
NVIDIA data-centre GPUs are allocated by NVIDIA and the server makers, and lead times are measured in months rather than weeks. Complete, tested systems usually get allocation sooner than loose parts, so we quote these platforms as built systems. Shipments outside the UK, and many advanced GPUs within it, need an end-user and end-use check under UK and US export rules before we can confirm supply.
Trade members get our engineers' time on sourcing and design questions, early stock and waitlist priority, and free technical support on every order.
A GPU cluster is the hardware. An AI factory is the whole production system around it: compute, networking, storage, software, facility and operations, designed together to produce AI output reliably. A cluster without the rest tends to run well below its potential.
There is no minimum. Many organisations start with a single 8-GPU server or one rack built to a reference design, then add identical racks. What matters is that the first step is built so it can grow without redesign.
No. Most customers use a UK colocation facility that can supply the power and cooling these systems need. We can help you choose one and handle the installation there. A suitable on-site server room also works if it passes our site survey.
Supply is the longest part. Lead times for NVIDIA data-centre platforms are currently measured in months, and depend on allocation. Design and the site survey run in parallel with supply, and installation and bring-up of a rack typically take days to a few weeks once the hardware is on site.
Sometimes. Advanced NVIDIA GPUs and AI systems are controlled under UK and US export rules. We need the end user, intended use and installation site before we can confirm supply, and some destinations need a licence that can take time or be refused.
We do. Hardware warranty and NVIDIA software support are arranged through us, and our UK engineers handle first-line support. Trade members get free technical support on every order, and SysOps247 adds around-the-clock monitoring and operations.
Tell us what you want it to produce, how many people will use it and where it would live. An engineer will come back with a sized recommendation and an honest view on whether owning makes sense for you.