SPECIAL OFFER: Dell PowerEdge XE9680 with 8x NVIDIA A100 GPUs - In Stock
- server-parts.eu server-parts.eu
- 6 days ago
- 3 min read
Updated: 5 days ago
This Dell PowerEdge XE9680 configuration is designed for AI training, inference workloads, and HPC environments requiring maximum compute density and memory bandwidth.
Dell PowerEdge XE9680 with 8x NVIDIA A100 GPUs
✔️ No Upfront Payment Required - Test First, Pay Later
Technical Specifications - Dell PowerEdge XE9680
Chassis - Dell PowerEdge XE9680:
6U, 8 x SFF NVMe bays
GPU Platform - Dell PowerEdge XE9680:
NVIDIA HGX A100 baseboard
8 x NVIDIA A100 80GB SXM GPUs
500W TDP per GPU
NVLink interconnect for high GPU-to-GPU bandwidth
Peak FP16: 312 TFLOPS per GPU (Tensor Cores)
Total GPU Memory: 640GB HBM2e
CPU Configuration - Dell PowerEdge XE9680:
2 x Intel Xeon Platinum 8470 (Sapphire Rapids)
104 cores / 208 threads total
105MB L3 cache
DDR5-4800 support
Base TDP: 350W per CPU
Turbo Boost and Hyper-Threading enabled
Memory - Dell PowerEdge XE9680:
64GB RDIMM DDR5-4800
Dual Rank ×16
Expandable depending on workload requirements
Networking - Dell PowerEdge XE9680:
1 x Broadcom 57508 Dual Port 100GbE QSFP, PCIe x16
1 x Mellanox ConnectX-6 Single Port HDR VPI (Infiniband), PCIe x16
Full height brackets on both cards
Suitable for RDMA, GPUDirect, and low-latency interconnects
Storage Bay - Dell PowerEdge XE9680:
8 x 2.5” NVMe U.2 hot-swap slots
PCIe Gen4 interface
Drives not included, configurable per application (e.g., AI dataset staging, OS boot, scratch)
Power and Cooling - Dell PowerEdge XE9680:
Redundant power supply units (rated for high-GPU draw)
Optimized airflow and cooling system for SXM GPU thermals
Additional Details - Dell PowerEdge XE9680:
3-year warranty included
Stock availability: Immediate
Location: EU warehouse
Shipping within EU: 4–8 days
Technical Analysis - Dell PowerEdge XE9680
The Dell PowerEdge XE9680 is a 6U GPU server, purpose-built for AI, machine learning, and HPC. It features the NVIDIA HGX A100 SXM4 platform, which includes 8× A100 80GB GPUs connected via NVLink 3.0, enabling direct GPU-to-GPU bandwidth of up to 600 GB/s. This topology is critical for data-parallel and model-parallel workloads where large tensors are exchanged between GPUs during training.
Each A100 provides 80GB of HBM2e memory with 2TB/s memory bandwidth, resulting in a combined 640GB GPU memory and 16TB/s total memory throughput across all GPUs. This configuration supports extremely large models and datasets without external paging or bottlenecks.
The server is powered by 2× Intel Xeon Platinum 8470 CPUs, offering a combined 104 cores and 208 threads. With DDR5-4800 support and a large 105MB shared L3 cache per CPU, the platform is optimized for high-throughput, multi-threaded execution. These CPUs have a TDP of 350W each, ensuring sustained performance under heavy load and avoiding CPU-side bottlenecks in GPU-accelerated workflows.
Network bandwidth is handled by two high-speed adapters: a Broadcom 57508 dual-port 100GbE (QSFP) PCIe NIC, and a Mellanox ConnectX-6 HDR Infiniband adapter capable of 200Gb/s. This gives system integrators flexibility for both Ethernet-based data center fabrics and RDMA/Infiniband-connected HPC clusters. PCIe Gen4 support ensures sufficient host-to-device and inter-device bandwidth, essential for storage, networking, and accelerator communication.
Overall, the XE9680 offers dense compute, memory, and I/O performance in a 6U chassis—making it one of the most capable GPU nodes available for on-premise AI and HPC infrastructure.
Suitable Use Cases - Dell PowerEdge XE9680
AI training with PyTorch or TensorFlow
LLM fine-tuning and deployment (e.g., LLaMA, Mistral, GPT-J)
GPU-accelerated simulation and modeling
Data center-scale HPC clusters
VPI and RDMA-capable interconnect for distributed workloads
If you’re running out of GPU capacity or need a fast upgrade for large model training, this XE9680 setup delivers maximum compute density without lead times.
Dell PowerEdge XE9680 with 8x NVIDIA A100 GPUs
✔️ No Upfront Payment Required - Test First, Pay Later
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