Brand: HPE | Category: GPUs
SKU: R8S80A | Part #: R8S80A | MPN: R8S80A
Contact for Pricing — Request a Quote
Generative AI training and LLM inference at scale demand a unified GPU architecture, and the HPE Cray XD670 AI Server delivers that through an 8x NVIDIA H100 SXM5 configuration with NVLink 4.0 and NVSwitch full all-to-all GPU mesh connectivity. This 2U rack-mount server (part number R8S80A) packs 640GB of total HBM3 GPU memory—80GB per GPU—enabling organizations to run large language models and deep learning workloads without constant data shuffling between nodes. Backed by dual Intel Xeon Scalable 4th Gen Sapphire Rapids processors, up to 2TB of DDR5 RDIMM system memory, and PCIe Gen 5 internal NVMe SSD support, the XD670 balances compute density with memory bandwidth for both training and inference pipelines. Direct liquid cooling—available as warm water rear-door or direct-to-chip configurations—keeps eight H100 GPUs operating efficiently within tight data center constraints, while redundant titanium-level power supplies ensure reliability in mission-critical deployments.
AI infrastructure teams, HPC procurement specialists, and enterprise data center architects are the primary buyers of this system because it collapses what traditionally required multiple single-GPU nodes into one compact form factor. The HPE Cray XD Series ecosystem includes the HPE Cray Programming Environment and HPE Machine Learning Development Environment (MLDE), reducing time-to-productivity for teams running generative AI, HPC simulation, and deep learning research. Dual high-speed network interfaces supporting NVIDIA ConnectX-7 or HPE Slingshot 11 HPC fabric integrate seamlessly into existing cluster topologies, while iLO 6 management and HPE Insight Cluster Cluster Management Utility support keep operational overhead minimal across large deployments. Support for Red Hat Enterprise Linux, SUSE Linux Enterprise Server, and Ubuntu ensures compatibility with standard enterprise IT stacks.
To specification the HPE Cray XD670 AI Server (R8S80A) for your organization's next-generation AI or HPC cluster, contact Omnixon Global—your B2B enterprise IT hardware distributor in Dubai, UAE—to submit a detailed RFQ.
| Brand | HPE |
| Category | GPUs |
| SKU | R8S80A |
| Part Number | R8S80A |
| Condition | New |
| Manufacturer Part Number | R8S80A |
| Product Line | HPE Cray XD Series |
| Form Factor | 2U rack-mount server |
| GPU Configuration | 8x NVIDIA H100 SXM5 80GB GPUs |
| GPU Interconnect | NVLink 4.0 and NVSwitch (full all-to-all GPU mesh) |
| GPU Memory per GPU | 80GB HBM3 |
| Total GPU Memory | 640GB HBM3 |
| CPU | Dual Intel Xeon Scalable (4th Gen, Sapphire Rapids) |
| PCIe Generation | PCIe Gen 5 |
| System Memory | Up to 2TB DDR5 RDIMM |
| Storage | Internal NVMe SSD support via PCIe Gen 5 slots |
| Network | Dual high-speed network interfaces supporting NVIDIA ConnectX-7 or HPE Slingshot 11 HPC fabric |
| Cooling | Direct Liquid Cooling (DLC) — warm water rear-door or direct-to-chip liquid cooling |
| Power Supply | Redundant high-efficiency power supplies (titanium-level efficiency) |
| Management | iLO 6 (Integrated Lights-Out) with HPE Insight Cluster Management Utility support |
| OS Support | Red Hat Enterprise Linux, SUSE Linux Enterprise Server, Ubuntu |
| Software Ecosystem | HPE Cray Programming Environment, HPE Machine Learning Development Environment (MLDE) |
| Rack Units | 2U |
| Target Workloads | Generative AI training, LLM inference, HPC simulation, deep learning |
The HPE Cray XD670 AI Server accelerates AI/ML training, inference, scientific HPC, and virtualization (vGPU) workloads. Typical deployments include LLM training clusters, computer-vision pipelines, financial risk modeling, and rendering farms.
Key specifications for the HPE Cray XD670 AI Server: new condition; raid support H200; gpu support NVIDIA H200; gpu count 8x; ai optimized Yes. Manufacturer part number R8S80A. For the full datasheet with electrical, environmental, and compliance details, contact our pre-sales engineering team.
The HPE Cray XD670 AI Server requires a PCIe Gen4 or Gen5 x16 slot, server power adequate for the card's TDP, and CUDA/ROCm driver support in your hypervisor or bare-metal OS. Sales engineering will confirm chassis fit (1U/2U/4U), PCIe lane count, and PSU headroom before quoting.