Typical configuration
4 GPU AI Training Server
- cpu
- AMD EPYC 9654 (DDR5 ECC)
- gpu
- 4× NVIDIA H100 (PCIe)
- ram
- 256GB DDR5 ECC
- storage
- 4× NVMe U.3 (3.84TB)
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.
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 this server type
Supports PCIe Gen4 / Gen5 for AI Training, Machine Learning to keep GPUs fed and raise throughput.
Validated Up to 8× GPUs against power and thermal envelopes for sustained utilisation.
Optimised NVMe storage support for AI Training, Machine Learning to cut staging latency and keep accelerators compute-bound.
Aligns DDR5 ECC memory bandwidth with batch throughput to avoid CPU bottlenecks during AI Training, Machine Learning.
Engineered Redundant PSUs and cooling headroom to hold thermal and electrical margins under sustained load.
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.
Architecture & key features
PCIe bandwidth & expansion
Supports PCIe Gen4 / Gen5 lanes and slot topology to minimise interconnect stalls and raise throughput.
GPU support & density
Provides Up to 8× GPUs expansion with sufficient power and thermal headroom for sustained utilisation.
Storage architecture (NVMe)
Uses NVMe storage support to reduce staging latency and improve checkpoint write bandwidth.
Cooling & power considerations
Engineered Redundant PSUs and cooling 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 HPC Server
- cpu
- Dual Intel Xeon Scalable (DDR5 ECC)
- gpu
- 8× NVIDIA A100
- ram
- 512GB DDR5 ECC
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.
Related pages
Configure your GPU rack server
Share your workload requirements; we spec, validate, and quote to deployment constraints.