UK GPU servers for AI & inference
Dedicated NVIDIA GPU servers — from a single L4 for inference up to multi-GPU H100 and H200 nodes for training — hosted in our privately owned Cambridge Tier III data centre. UK data sovereignty, fixed monthly pricing, real engineers.
NVIDIA H100 / H200
Training-ready
Cluster fabric
100 GbE
H100 / H200
Latest NVIDIA silicon
100 GbE
Cluster networking
Tier III
UK Cambridge DC
24/7
UK NOC support
Built for accelerated workloads
Every component — CPU, memory, storage, network — chosen so the GPU is never the bottleneck.
NVIDIA Data Centre GPUs
From a single L4 or L40S for inference up to multi-GPU H100 and H200 nodes for training — the right accelerator for the workload, not whatever's on the shelf.
High-Core EPYC & Xeon
AMD EPYC Genoa and Intel Xeon Scalable hosts paired with PCIe Gen 5 risers so your GPUs are fed, not throttled by the CPU.
Up to 2 TB DDR5 ECC
Plenty of headroom for large datasets, vector databases and in-memory pre-processing pipelines — registered ECC across every channel.
NVMe Gen 4/5 Storage
Local NVMe scratch for training data and checkpoints, plus optional all-flash shared storage over 25/100 GbE for multi-node jobs.
NVLink & PCIe Gen 5
NVLink-bridged pairs for tensor-parallel training, and full-bandwidth PCIe Gen 5 lanes per GPU on supported platforms.
25 / 100 GbE Networking
Low-latency 25 GbE as standard, with 100 GbE uplinks available for distributed training and high-throughput inference clusters.
Private UK Data Centre
Hosted in our own Cambridge Tier III facility — N+1 power, N+N cooling, biometric access and data sovereignty inside the UK.
GPU Specialist NOC
UK engineers who actually know CUDA driver stacks, NCCL, IOMMU passthrough and the quirks of running GPUs at scale.
GPUs we provision
Pick by workload, not by what's in stock. We source the right SKU for the job.
| GPU | Role | VRAM | Best for |
|---|---|---|---|
| NVIDIA L4 | Inference & video | 24 GB | Cost-effective inference, transcoding, light fine-tuning |
| NVIDIA L40S | Inference & graphics | 48 GB | Mid-size LLM inference, Stable Diffusion, virtual workstations |
| NVIDIA A100 80GB | Training & HPC | 80 GB HBM2e | Mainstream training, scientific compute, multi-tenant |
| NVIDIA H100 SXM/PCIe | Large-model training | 80 GB HBM3 | Transformer training, FP8 inference at scale |
| NVIDIA H200 | Frontier LLMs | 141 GB HBM3e | 70B+ parameter models, long-context inference |
| NVIDIA RTX 6000 Ada | Workstation / render | 48 GB | VFX, CAD/CAE, GPU-accelerated rendering |
Other accelerators (AMD Instinct MI300X, NVIDIA Grace Hopper) available on request, subject to lead time.
Workloads we run every day
From a single inference node to multi-rack training clusters, all from one UK provider.
AI / ML training
Multi-GPU nodes with NVLink, NCCL-tuned networking and fast local NVMe for checkpoints — train LLMs, vision and recommender models on dedicated hardware.
LLM inference
Single- and multi-GPU inference for open-source models — Llama, Mistral, Qwen and others — served from the UK with low-latency 100 GbE.
Rendering & VFX
RTX 6000 Ada and L40S nodes for render farms, real-time 3D, virtual production pipelines and GPU-accelerated transcoding.
Scientific & HPC
CUDA, OpenCL and MPI-friendly hosts for simulation, genomics, computational finance and research computing.
Single nodes to multi-rack training clusters
Start with one GPU server and grow into a private cluster on the same fabric, in the same hall, managed by the same engineers.
- Private VLANs & 100 GbE fabric — leaf/spine non-blocking network for NCCL all-reduce and high-throughput inference.
- Shared NVMe storage — optional all-flash storage targets over NFS, S3 or NVMe-oF for multi-node datasets and checkpoints.
- Orchestration ready — Slurm, Kubernetes (with the NVIDIA GPU Operator), Ray and Determined.AI all run cleanly on our hosts.
- IPMI/BMC & serial — out-of-band access on every node so you can recover, reinstall and console without us.
- Dense power & cooling — A+B feeds and N+N cooling sized for high-wattage GPU nodes (up to ~10 kW per rack as standard, higher on request).
Example 4×H100 node
Chassis : 4U GPU host, redundant 2 + 2 PSU
CPU : 2 x AMD EPYC 9354 (32C / 64T each)
Memory : 1 TB DDR5-4800 ECC RDIMM
GPUs : 4 x NVIDIA H100 80GB (PCIe Gen 5, NVLink-bridged pairs)
Storage : 2 x 1.92 TB NVMe (OS, RAID 1)
4 x 7.68 TB NVMe Gen 4 (scratch, RAID 0)
Network : 2 x 25 GbE (LACP) + 1 x 100 GbE cluster uplink
Management : Dedicated IPMI / BMC, serial console
OS / stack : Ubuntu 22.04 LTS, NVIDIA driver, CUDA 12,
cuDNN, Docker + NVIDIA Container ToolkitShapes are illustrative — every build is sized to your model, batch size and latency targets.
How to get started
01
Scope your workload
Tell us about your models, dataset size, batch sizes and latency targets. We'll recommend the right GPU, host platform, network and storage shape.
02
We build & rack
We source and rack the node in our Cambridge data centre, install your OS or hypervisor, configure drivers, CUDA, NCCL and networking, and hand over remote IPMI/BMC.
03
Deploy & scale
Go live with full 24/7 UK NOC support. Add nodes, switch GPU SKUs or scale to a multi-node cluster on a private VLAN as your workload grows.
8 common questions