Typical configuration
8-GPU PCIe Training Node
- chassis
- ASUS ESC8000A-E13 (4U)
- cpu
- Dual AMD EPYC 9005
- gpu
- 8× NVIDIA H200 NVL (PCIe)
- ram
- 1.5TB DDR5 ECC
- storage
- NVMe U.2 scratch + cache
- network
- 2× 100GbE
Enterprise rack servers
AI Training Servers
Multi-GPU systems engineered for deep learning, LLM training, and distributed workloads.
- 4×–8× datacenter GPUs
- NVLink / NVSwitch support
- High-throughput networking (100–400GbE)
- NVMe scratch and cache layers
AI training servers designed for multi-GPU scaling and sustained utilisation. GPU interconnect, NVMe staging, and high-throughput networking reduce idle cycles during distributed training.
- Delivered pre-built and fully tested
- Instant configuration & quotes
- 24/7 expert support across Europe
Configure a AI Training Server
Experts in configuring AI Training Servers
At a glance
4×–8× GPU configurations
NVLink / NVSwitch interconnect
AMD EPYC / Intel Xeon (DDR5 ECC)
High-speed NVMe scratch storage
100GbE / 400GbE networking
Redundant PSUs and thermal headroom
Why choose AI Training Servers for Deep Learning Workloads
NVSwitch on HGX SXM platforms, and NVLink bridges on H200 NVL cards, let GPUs exchange gradients directly instead of over PCIe.
100GbE to 400GbE networking keeps multi-node data-parallel and model-parallel jobs from stalling on the network.
NVMe scratch tiers hold shuffled datasets and frequent checkpoints, so restarting after a failure costs minutes rather than hours.
Configurations range from 4-GPU nodes for fine-tuning to eight-GPU NVIDIA HGX B200 systems for large-scale training.
Redundant power supply options protect multi-day jobs, where an unplanned shutdown wastes days of compute.
Use cases
- LLM training and fine-tuning
Reduces step time by maintaining GPU utilisation during forward/backward passes and gradient exchange.
- Computer vision models
Improves throughput by eliminating data-loader bottlenecks with NVMe staging.
- Distributed training clusters
Scales efficiently across nodes using high-bandwidth interconnects and low-latency networking.
- Research and experimentation
Supports long-running jobs with stable thermals, consistent I/O, and predictable performance.
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
GPU interconnect
NVSwitch gives all-to-all GPU bandwidth on HGX SXM platforms. Among PCIe cards, H200 NVL supports NVLink bridges; RTX PRO 6000 Blackwell does not.
Cluster networking
RDMA-capable network adapters at 100GbE and above can be specified for distributed training.
Checkpoint storage
Local NVMe arrays sized for frequent checkpoint writes and dataset caching.
Host platform
AMD EPYC or Intel Xeon with DDR5 ECC and enough PCIe lanes to run GPUs, NICs and NVMe at full width.
Example builds
Representative configurations — every build is tailored to your workload and environment.
Typical configuration
8-GPU HGX B200 Training Node
- chassis
- Supermicro AS-A126GS-TNBR (10U)
- cpu
- Dual AMD EPYC 9005
- gpu
- 8× NVIDIA B200 SXM (HGX, NVSwitch)
- ram
- 2TB DDR5 ECC
- storage
- NVMe U.2 scratch + cache
- network
- 400GbE
Variants & options
Single-node training
4–8 GPU systems for contained training workloads without cluster overhead.
Multi-node clusters
Distributed training across nodes with high-speed interconnect and synchronisation.
Hybrid training + inference
Balanced systems supporting both model training and deployment workloads.
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 AI training server
Define your model, dataset size, and scaling requirements — we’ll architect and quote accordingly.