Typical configuration
4 GPU AI Server
- chassis
- ASUS ESC4000A-E12 (2U)
- cpu
- Single AMD EPYC 9005
- gpu
- 4× NVIDIA H200 NVL or RTX PRO 6000 Blackwell (PCIe)
- ram
- 512GB DDR5 ECC
- storage
- NVMe U.2 / U.3
Enterprise rack servers
GPU Rack Servers
Built for AI training, inference and high-performance compute.
- Up to 8× GPUs per node
- PCIe Gen4 / Gen5 bandwidth
- AMD EPYC / Intel Xeon platforms
- High-speed NVMe storage
GPU rack servers tuned for multi-accelerator training and inference. PCIe lane budget and NVMe scratch staging reduce GPU wait time; power and cooling sustain utilisation.
- Delivered pre-built and fully tested
- Instant configuration & quotes
- 24/7 expert support across Europe
Configure a GPU Rack Server
Experts in configuring GPU Rack Servers
At a glance
Up to 8× GPUs
PCIe Gen4 / Gen5
AMD EPYC / Intel Xeon
DDR5 ECC memory bandwidth
NVMe storage support
Redundant PSUs and cooling headroom
Why choose GPU Rack Servers for AI & HPC
GPU chassis range from 1U and 2U nodes with one to four GPUs, through 4U and 5U systems with eight to ten PCIe GPUs, to a 10U NVIDIA HGX B200 platform.
Each card connects to the host over PCIe 5.0; chassis with ten or more GPUs use PCIe switches to share CPU lanes between cards.
Single- and dual-socket EPYC or Xeon hosts supply the cores and memory channels that data loaders and preprocessing need alongside the GPUs.
Every GPU chassis is offered with redundant power supply options rated for its maximum GPU configuration.
Local NVMe holds datasets and checkpoints next to the GPUs, cutting reliance on shared network storage.
Use cases
- AI Training
Cuts step time by keeping multi-GPU training compute-bound during ingestion and gradient exchange.
- Machine Learning
Improves inference throughput with low-latency batch scheduling across nodes.
- Rendering
Reduces render latency by sustaining GPU pipeline saturation for VFX and 3D workloads.
- High Performance Compute
Improves parallel job throughput by keeping accelerator memory and I/O paths fed during long runs.
Not sure where to start? Talk to an engineer.
Our team will spec the right platform for your workload, validate compatibility, and turn it around fast.
Architecture & key features
PCIe and SXM options
Double-width PCIe 5.0 GPU slots on most chassis, with eight NVIDIA HGX B200 SXM GPUs on NVSwitch for all-to-all bandwidth on the largest platform.
Host CPU and memory
AMD EPYC or Intel Xeon with DDR5 ECC. NVIDIA's DGX systems pair roughly 1.5 to 3 times as much system RAM as GPU memory, a sensible starting point for training.
Power delivery
Redundant PSUs on 200–240V feeds. A fully loaded 8-GPU node can draw several kilowatts, and HGX B200 systems over 10kW.
Airflow
Front-to-back cooling with high-static-pressure fans, built for accelerators that have no fans of their own.
Example builds
Representative configurations — every build is tailored to your workload and environment.
Typical configuration
8 GPU AI & HPC Server
- chassis
- ASUS ESC8000A-E13 (4U)
- cpu
Variants & options
2 GPU Servers
2-GPU nodes for inference and small training runs with stable thermals.
4 GPU Servers
4-GPU systems that balance PCIe expansion, power, and sustained throughput.
8 GPU Servers
8-GPU chassis sized for dense deployments and sustained utilisation.
Instant Quotes
Configure and price in minutes
Guaranteed Compatible
If it's listed, it works together
Pre-Built & Tested
Full burn-in before it leaves us
UK Headquarters
Built, tested, and shipped from the UK
Related pages
Configure your GPU rack server
Share your workload requirements; we spec, validate, and quote to deployment constraints.