Dell XE9680 AI Benchmark

FAQs

• What is the Dell PowerEdge XE9680, and what key challenges in enterprise AI infrastructure is it designed to overcome?

  The Dell PowerEdge XE9680 is a flagship 8-GPU, 6U server engineered to move enterprises from experimental AI projects to full-scale production with minimal friction. It is specifically designed to handle the demands of modern GenAI workflows, such as training and deploying large models like GPT-style multimodal systems or Llama 3.1, which require massive compute density, ultra-fast interconnects, and terabytes of shared GPU memory bandwidth. By providing computational performance and operational efficiency, the XE9680 aims to solve infrastructure bottlenecks that Gartner predicts will cause over 60% of AI projects to fail to move beyond pilot stages by the end of 2025.

• What foundational architectural elements support the high-performance AI operations of the XE9680?

  The architecture is built to sustain high-performance AI operations, including model training and multi-GPU inference. At the compute layer, the server utilizes dual 4th or 5th Gen Intel Xeon Scalable processors, providing up to 64 cores per socket for parallel workloads. Memory bandwidth is optimized with support for up to 4TB of DDR5 RDIMM running at speeds up to 5600 MT/s, which significantly reduces latency when moving large datasets between CPU and GPU memory. For storage and I/O, the system features PCIe Gen 5.0 lanes and can support up to 16 E3.S NVMe direct drives, achieving up to 122.88 TB of storage capacity, which is essential for rapid access to datasets and model checkpoints. These combined capabilities translate into consistent low-latency access, faster data movement, and higher throughput, which are critical for real-time analytics, HPC modeling, and AI training.

• How does the XE9680 provide flexibility in accelerator choice, and what are the key offerings for GPUs?

  The XE9680 is distinguished by its flexible 8-way GPU ecosystem, allowing organizations to choose between NVIDIA, AMD, or Intel accelerators without requiring a redesign of the underlying infrastructure. This agnostic design future-proofs the AI infrastructure, enabling organizations to switch accelerators as frameworks evolve. The system supports up to 1.5TB of shared coherent GPU memory. Specific accelerator options include: 

  • NVIDIA HGX H100/H200 (SXM5): Offers up to 141GB of memory per GPU, uses the CUDA ecosystem and NVLink interconnect, and is optimized for inference throughput. 

  • AMD Instinct MI300X (OAM): Provides 192GB of HBM3 memory per GPU, making it ideal for massive language models. 

  • Intel Gaudi 3 (OAM): Features 128GB of memory and utilizes native RoCE ports, simplifying Ethernet-based multi-node scaling. 
• What are the performance advantages and benchmark results for the XE9680 in terms of AI training and inference efficiency?

  The 8-GPU configuration delivers exceptional results in demanding workloads, such as GPT-style transformer models and BERT pre-training. In tests, XE9680 systems with NVIDIA H100 SXM5 GPUs achieved up to 1.8× faster BERT pre-training compared to previous-generation XE8545 systems. Due to the integration of NVSwitch + NVLink 4.0, the server demonstrates linear scaling when increasing from 4 to 8 GPUs, indicating minimal communication bottlenecks. Sustained GPU utilization remains above 95% under full thermal load, supported by Dell’s balanced airflow and liquid-assisted cooling. For inference workloads, the server demonstrates up to 2× higher inference throughput when using H100 GPUs with Transformer Engine optimizations. It supports FP8 precision to cut latency while preserving accuracy, which is highly beneficial for real-time recommendation or conversational AI deployments.

• What are the key economic and operational considerations, such as power efficiency and security, for the XE9680?

  The XE9680 offers impressive energy discipline even with eight high-wattage GPUs. A full configuration with 8× NVIDIA H100 SXM GPUs draws approximately 5,586W. Organizations can manage the performance-cost tradeoff: NVIDIA configurations often provide the best performance-per-watt for inference, while AMD MI300X variants can offer 10–20% acquisition savings and higher HBM3 memory, making them practical for TCO-conscious, large-model training deployments. Operationally, the system is secured and managed using iDRAC9 and OpenManage Enterprise, which allows administrators to monitor thermals, update firmware, and automate lifecycle management remotely. Security is anchored in Dell’s Cyber Resilient Architecture, featuring a Silicon-based Root of Trust, TPM 2.0, and cryptographically signed firmware to protect against tampering.

• How should organizations choose the correct XE9680 configuration based on their specific workload priorities?

  The configuration choice depends on specific goals regarding memory constraints, latency needs, and cost constraints. A decision framework based on workload priority suggests: 

  • Memory-Heavy Training: The AMD Instinct MI300X configuration is recommended because its 192GB HBM3 per GPU provides exceptional throughput for dense workloads, allowing it to train or host frontier LLMs such as Llama 3.1 405B. 

  • Low-Latency Inference: NVIDIA Hopper (H100/H200) is the clear leader due to its mature CUDA stack and precision efficiency, delivering 1.5× higher TOPS through INT8 precision optimization and Tensor Cores, ideal for production chatbots. 

  • Cost Optimization: The AMD Instinct MI300X provides a practical edge for TCO-conscious deployments by offering 10–20% hardware cost savings and open-source ROCm software flexibility. 

  • Multi-Model Workloads: Intel Gaudi 3 is a strong fit for distributed AI environments because it integrates native RoCE Ethernet connectivity, simplifying cluster expansion. 
• What support is available for enterprises looking to deploy and optimize the Dell PowerEdge XE9680?

  Enterprises do not need to build their XE9680 environment from scratch; assistance is available to deploy, integrate, and optimize these systems. Support includes providing pre-validated configurations tested for real AI and HPC workloads. Integration services ensure the XE9680 fits smoothly into existing monitoring and orchestration stacks, such as Grafana, Prometheus, Slurm, or NVSM. Furthermore, performance validation, including multi-node stress tests and GPU interconnect checks, is performed to confirm reliable performance under peak loads. Operational enablement is also provided through 24/7 support, documentation, and training to ensure long-term optimization.