Dell PowerEdge XE9680 8x H200 SXM5 GPU Server

The Dell PowerEdge XE9680 configured with eight NVIDIA H200 SXM5 GPUs represents one of the most capable AI infrastructure platforms available for enterprise data centers. Built on NVIDIA's Hopper architecture, the H200 SXM5 introduces HBM3e memory — delivering 141 GB of high-bandwidth memory per GPU and aggregate memory bandwidth of 3.35 TB/s per GPU — a substantial advancement over its predecessor that directly addresses the memory-capacity bottlenecks encountered in large-scale generative AI and deep learning model training. The XE9680 chassis is purpose-engineered to support eight full-height SXM5 modules interconnected via NVLink 4.0 and NVSwitch technology, enabling GPU-to-GPU bandwidth of up to 900 GB/s bidirectional, effectively presenting the eight GPUs as a unified, high-throughput compute fabric.

The server platform itself is a 10U rack-optimized system supporting dual 4th Generation Intel Xeon Scalable processors, high-capacity DDR5 system memory, and a PCIe Gen 5 fabric that sustains the I/O demands of simultaneous multi-GPU inference and training jobs. Connectivity options include high-speed OCP 3.0 network adapters supporting 400GbE or InfiniBand NDR, ensuring the node can participate in tightly coupled distributed training clusters across hundreds or thousands of GPU nodes with minimal interconnect latency. The integrated NVMe storage subsystem, combined with Dell's OpenManage systems management stack, provides the operational visibility and storage performance required for enterprise-grade AI data pipelines.

Designed for organizations deploying frontier-scale AI workloads — including training of large language models, multimodal foundation models, and high-throughput inference serving — the XE9680 with H200 SXM5 GPUs is validated for deployment in hyperscale data centers and enterprise AI factories. The combination of HBM3e capacity, NVLink 4.0 interconnect, and the thermally engineered XE9680 chassis enables sustained peak compute across extended training runs, making it the reference platform for organizations in the UAE, GCC, EMEA, and APAC regions requiring maximum GPU density and memory headroom per rack unit.

Ideal for

• Training and fine-tuning of large language models and multimodal foundation models with parameter counts exceeding 70 billion, where HBM3e memory capacity eliminates model-parallelism overhead
• High-throughput AI inference serving for enterprise generative AI applications requiring low latency and high concurrent request handling across large context windows
• Scientific computing and simulation workloads in life sciences, computational fluid dynamics, and climate modeling that benefit from the unified high-bandwidth GPU memory fabric
• Multi-tenant AI platform deployments where GPU virtualization via NVIDIA MIG (Multi-Instance GPU) allows secure workload isolation across internal teams or business units
• Data center AI factory builds requiring maximum GPU compute density per rack, leveraging the 8-GPU SXM5 configuration to consolidate training capacity and reduce inter-node communication overhead
• Sovereign AI infrastructure initiatives in EMEA and APAC where governments and national enterprises require on-premises, data-sovereign AI compute at frontier model scale

Technical specifications

Available from Omnixon Global. Submit an RFQ and our team will confirm configuration and availability for your order.