Dell PowerEdge XE9680 GPU Server (8x NVIDIA H200)

The Dell PowerEdge XE9680 is a purpose-built 6U rack server engineered specifically for large-scale AI training and high-performance computing workloads. It houses eight NVIDIA H200 SXM5 GPUs interconnected via NVLink 4.0 and NVSwitch technology, delivering a unified high-bandwidth GPU fabric that enables all-to-all GPU communication at scale. The H200 GPU introduces HBM3e memory with 141 GB capacity per GPU, providing substantially higher memory bandwidth compared to its predecessor and enabling larger model parameter sets to reside entirely on-device during training runs.

The XE9680 is built on a dual-socket Intel Xeon Scalable or AMD EPYC processor platform and supports PCIe Gen 5 host connectivity, ensuring the CPU-to-GPU data path does not become a bottleneck during data ingestion and preprocessing pipelines. The system accommodates high-throughput NVMe storage directly within the chassis and provides multiple OCP 3.0 network adapter slots to support 400GbE or InfiniBand NDR fabric connectivity, which is essential for multi-node GPU cluster scaling. Integrated Dell OpenManage systems management and iDRAC9 with Lifecycle Controller provide enterprise-grade remote monitoring, telemetry, and configuration capabilities across the full system stack.

Designed for deployment in hyperscale and enterprise AI infrastructure, the XE9680 targets generative AI model training, large language model fine-tuning, and scientific simulation workloads that demand the highest available GPU memory capacity and interconnect bandwidth. Its 6U form factor is optimized for high-density data center deployment and direct liquid cooling readiness, addressing the substantial thermal output generated by eight H200 GPUs operating simultaneously at full compute capacity.

Ideal for

• Large language model pre-training and fine-tuning, where the 1,128 GB aggregate HBM3e GPU memory across eight H200s enables multi-hundred-billion parameter models to be trained with reduced pipeline parallelism overhead
• Generative AI inference serving for enterprise deployments requiring simultaneous multi-model or large single-model serving with high token throughput and low latency
• Multimodal AI research and development, including vision-language model training that demands concurrent processing of high-resolution image tensors and large text corpora
• High-performance computational science simulations such as molecular dynamics, climate modeling, and computational fluid dynamics that leverage GPU-accelerated solvers across tightly coupled multi-GPU domains
• Enterprise AI platform build-out for centralized GPU compute clusters, serving multiple internal teams through orchestration layers such as Kubernetes with GPU operator or Slurm-based HPC schedulers
• Retrieval-augmented generation pipeline development and vector embedding generation at scale, where sustained GPU memory bandwidth directly determines throughput for embedding model inference

Technical specifications

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