Dell PowerEdge XE9680 8-GPU Server Configured with H200 SXM5 141GB (8× GPU)

The Dell PowerEdge XE9680 is a purpose-built, high-density 8-GPU rack server engineered for the most demanding AI training, large-scale inference, and HPC workloads in enterprise and hyperscale datacenter environments. Configured with eight NVIDIA H200 SXM5 GPUs each carrying 141 GB of HBM3e memory, the system delivers an aggregate 1,128 GB of GPU memory across the node, enabling training of frontier-scale large language models and multimodal AI architectures that cannot fit within smaller GPU memory footprints. The XE9680 platform utilizes the NVLink and NVSwitch fabric interconnecting all eight GPUs at full bandwidth, eliminating PCIe bottlenecks and providing the all-to-all GPU communication throughput essential for tensor-parallel and pipeline-parallel distributed training regimes.

The server is based on a dual-socket Intel Xeon Scalable (Sapphire Rapids) processor platform, supporting high-core-count configurations with DDR5 system memory across 32 DIMM slots, delivering substantial CPU-side bandwidth to feed the GPU subsystem. The chassis design incorporates a high-performance NVMe storage subsystem for fast checkpoint I/O and dataset staging, alongside redundant high-efficiency platinum-rated power supplies sized to meet the thermal design power demands of the full 8-GPU configuration. Rear-facing high-density networking options support NVIDIA ConnectX-7 or BlueField-3 based InfiniBand and Ethernet adapters, enabling low-latency, high-bandwidth interconnects required for multi-node GPU cluster scale-out.

Designed for enterprise IT procurement and integration, the XE9680 ships with Dell's OpenManage systems management suite and is compatible with standard DCIM tooling, iDRAC 9 out-of-band management, and Redfish API-based automation. The platform is validated for AI frameworks including PyTorch and TensorFlow, and aligns with NVIDIA DGX-ready ecosystem certifications. It is available through Omnixon Global for enterprise buyers across the UAE, GCC, EMEA, and APAC regions, providing a commercially supported, datacenter-ready alternative to dedicated AI appliance platforms while retaining the open-architecture flexibility of a standards-based rack server.

Ideal for

• Pre-training and fine-tuning of large language models (LLMs) with 70B+ parameter counts requiring terabytes of aggregate GPU memory across tensor-parallel configurations
• High-throughput generative AI inference serving for enterprise LLM deployments where large model shards must reside in GPU memory to meet latency SLAs
• Scientific HPC simulation workloads in computational fluid dynamics, molecular dynamics, and climate modeling leveraging FP64 and FP32 compute across all eight GPUs
• Computer vision pipeline training at scale, including diffusion models, vision transformers, and video generation models demanding sustained high-bandwidth memory throughput
• Multi-GPU reinforcement learning from human feedback (RLHF) pipelines requiring tightly coupled GPU-to-GPU communication via NVLink for reward model and policy model co-training
• Enterprise AI platform infrastructure serving as a shared multi-tenant GPU node within Kubernetes-based GPU orchestration clusters for internal model development teams

Technical specifications

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