Dell PowerEdge XE9680 GPU Server (8x NVIDIA H100/H200 SXM)

The Dell PowerEdge XE9680 is a purpose-built, rack-optimized AI training server engineered to house eight NVIDIA H100 or H200 SXM-form-factor GPUs within a 10U chassis. Each GPU is interconnected via NVIDIA NVLink and NVSwitch fabric, delivering a fully non-blocking, all-to-all GPU interconnect with up to 900 GB/s bidirectional bandwidth per GPU—enabling the tight coupling required for large-scale distributed deep learning. The platform pairs GPU compute with dual fourth-generation Intel Xeon Scalable processors and supports high-bandwidth DDR5 system memory, ensuring the CPU subsystem does not become a bottleneck during data preprocessing or mixed-precision inference pipelines.

Storage and networking are provisioned to match the sustained throughput demands of modern foundation-model training runs. The XE9680 supports multiple PCIe Gen 5 expansion slots for high-speed network adapters, including NVIDIA ConnectX-7 InfiniBand HDR/NDR or 400GbE NICs, and integrates NVMe storage bays capable of serving training datasets at the speeds necessary to keep eight SXM GPUs fully saturated. The chassis incorporates a rear-door heat exchanger-compatible thermal architecture and direct liquid cooling readiness, addressing the substantial thermal dissipation requirements of SXM GPU modules operating at full TDP.

Positioned as a flagship node for large-scale AI infrastructure deployments, the XE9680 targets organizations building or expanding GPU clusters for generative AI, large language model pre-training, and scientific simulation. It is managed through Dell's OpenManage ecosystem and supports integration with orchestration platforms such as Kubernetes with GPU Operator, SLURM, and NVIDIA Base Command Manager, making it operationally suitable for multi-tenant HPC and enterprise AI environments. Following its launch in 2023 Q3, global demand driven by generative AI adoption has created significant supply constraints on this platform.

Ideal for

• Pre-training and fine-tuning of large language models (LLMs) with hundreds of billions of parameters across multi-node GPU clusters
• Generative AI model development including text-to-image, multimodal, and diffusion model training at enterprise scale
• High-performance scientific simulation and computational fluid dynamics workloads requiring tightly coupled GPU parallelism
• Large-scale recommender system training for e-commerce, media streaming, and digital advertising platforms
• Distributed reinforcement learning from human feedback (RLHF) pipelines for enterprise AI alignment and safety research
• Drug discovery and molecular dynamics simulation leveraging GPU-accelerated frameworks such as GROMACS and OpenFold

Technical specifications

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