Typical configuration
4 GPU Training Node
- cpu
- AMD EPYC 9554 (DDR5 ECC)
- gpu
- 4Γ NVIDIA H100 (PCIe)
- ram
- 512GB DDR5 ECC
- storage
- 4Γ NVMe U.3 (3.84TB)
- network
- 2Γ 100GbE
Enterprise rack servers
Multi-GPU systems engineered for deep learning, LLM training, and distributed workloads.
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.

Experts in configuring AI Training Servers


Supports PCIe Gen4/Gen5 lanes for LLM training and fine-tuning, Computer vision models to keep GPUs fed and raise throughput.
Validated 4Γβ8Γ 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.
Reduces step time by maintaining GPU utilisation during forward/backward passes and gradient exchange.
Improves throughput by eliminating data-loader bottlenecks with NVMe staging.
Scales efficiently across nodes using high-bandwidth interconnects and low-latency networking.
Supports long-running jobs with stable thermals, consistent I/O, and predictable performance.
GPU support & density
Provides 4Γβ8Γ 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.
Representative configurations β every build is tailored to your workload and environment.
Typical configuration
Typical configuration
4β8 GPU systems for contained training workloads without cluster overhead.
Distributed training across nodes with high-speed interconnect and synchronisation.
Balanced systems supporting both model training and deployment workloads.
Define your model, dataset size, and scaling requirements β weβll architect and quote accordingly.