Typical configuration
4 GPU Training Node
- cpu
- AMD EPYC 9554 (DDR5 ECC)
- gpu
- 4× NVIDIA H100 (PCIe)
- ram
- 512GB DDR5 ECC
- storage
- 4× NVMe U.3 (3.84TB)
- 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.
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 this server type
Supports PCIe Gen4/Gen5 lanes for LLM training and fine-tuning, Computer vision models to keep GPUs fed and raise throughput.
Validated 4×–8× GPU configurations against power and thermal envelopes for sustained utilisation.
Optimised High-speed NVMe scratch storage for LLM training and fine-tuning, Computer vision models to cut staging latency and keep accelerators compute-bound.
Aligns AMD EPYC / Intel Xeon (DDR5 ECC) with batch throughput to avoid CPU bottlenecks during LLM training and fine-tuning, Computer vision models.
Engineered Redundant PSUs and thermal headroom to hold thermal and electrical margins under sustained load.
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.
Architecture & key features
GPU support & density
Provides 4×–8× GPU configurations expansion with sufficient power and thermal headroom for sustained utilisation.
Storage architecture (NVMe)
Uses High-speed NVMe scratch storage to reduce staging latency and improve checkpoint write bandwidth.
Cooling & power considerations
Engineered Redundant PSUs and thermal headroom for stable thermal and electrical margins under sustained load.
Example builds
Representative configurations — every build is tailored to your workload and environment.
Typical configuration
8 GPU Distributed Training Node
- cpu
- Dual AMD EPYC 9654
- gpu
- 8× NVIDIA H100 (SXM, NVLink)
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.
Related pages
Configure your AI training server
Define your model, dataset size, and scaling requirements — we’ll architect and quote accordingly.