⚡ Dell XE9680 vs NVIDIA DGX B200
AI-powered analysis across 25 matched specifications
Performance Overview
Scores based on quantifiable specification values (1-10 scale)
Detailed Specifications
| Specification | PowerEdge XE9680 Dell | NVIDIA DGX B200 NVIDIA DGX |
|---|---|---|
| Key Metrics | ||
| Form Factor | 6U Rack Server | 10 RU (444mm H × 482.2mm W × 897.1mm D) |
| GPU Configuration | 8× GPUs per Node (H100 / H200 / MI300X / Gaudi3) | 8× NVIDIA Blackwell GPUs |
| Total GPU Memory | 1.12TB GPU Memory (8× H200 NVLink pool) | 1,440 GB (1.4 TB) HBM3e |
| GPU Interconnect Bandwidth | 900GB/s GPU Bandwidth (NVLink full-mesh) | 14.4 TB/s aggregate NVLink |
| AI Performance (FP8 Training) | -- | 72 PFLOPS (with sparsity) |
| AI Performance (FP4 Inference) | -- | 144 PFLOPS (with sparsity) |
| Performance Claim vs Previous Generation | -- | 15× Faster (LLM inference vs H100) |
| Compute | ||
| Processor | Dual Intel Xeon Scalable 5th Gen (up to 64 cores) or 4th Gen (up to 56 cores) | 2x Intel Xeon Platinum 8570, 112 cores, 2.1 / 4.0 GHz |
| Memory | ||
| Memory | 32 DDR5 DIMMs, up to 4 TB @ 5600 MT/s (5th Gen) / 4TB System Memory (32 DIMMs DDR5 5600 MT/s) | 2 TB (configurable to 4 TB) |
| Storage | ||
| Storage | 8× 2.5" NVMe/SAS/SATA or 16× E3.S NVMe — up to 122.88 TB | OS: 2x 1.9 TB NVMe M.2; Internal: 8x 3.84 TB NVMe U.2 |
| Networking | ||
| Networking | 1× OCP 3.0 + 2× 1GbE LOM; PCIe InfiniBand/Ethernet via slots | 8x ConnectX-7 VPI OSFP (400 Gb/s) + 2x BlueField-3 DPU |
| GPU / Accelerators | ||
| GPU Accelerators | 8× NVIDIA HGX H200 (141 GB) / H100 (80 GB) / H20; 8× AMD MI300X (192 GB); 8× Intel Gaudi3 (128 GB) | 8x NVIDIA Blackwell GPUs |
| GPU Interconnect | NVLink full-mesh 900 GB/s (NVIDIA) / Infinity Fabric (AMD) | 2x NVLink Switch System — 14.4 TB/s aggregate |
| GPU Memory Bandwidth | -- | 64 TB/s bandwidth |
| Expansion / PCIe | ||
| PCIe Expansion | 10× PCIe Gen5 x16 (8 with Intel Gaudi3) | -- |
| Management | ||
| Management | iDRAC9 Enterprise / Datacenter, Redfish API, CloudIQ, OpenManage Enterprise | -- |
| Power | ||
| Power Supplies | 6× 3200W Titanium (277V) / 2800W Titanium (200–240V), 3+3 FTR | -- |
| Power Consumption | -- | ~14.3 kW max |
| Physical / Environmental | ||
| Cooling | Air-cooled, 16 fans, 1200 CFM, 10–35°C ambient | Air-cooled chassis |
| Dimensions | 10.36" H × 18.97" W × 39.71" D (with bezel) | 10 RU (444mm H × 482.2mm W × 897.1mm D) |
| Weight | Up to 251 lbs / 114 kg (configuration dependent) | -- |
| Security | ||
| Security | Silicon Root of Trust, Secure Boot, Secure Component Verification, SEDs | -- |
| Software & OS Compatibility | ||
| OS Support | -- | NVIDIA DGX OS, Ubuntu, Red Hat Enterprise Linux, Rocky |
| Software | -- | NVIDIA AI Enterprise, NVIDIA Mission Control, NVIDIA Run:ai |
| Warranty & Support | ||
| Support | -- | 3-year business-standard hardware and software support |
Expert Analysis
The Dell PowerEdge XE9680 and NVIDIA DGX B200 represent two distinct approaches to high-performance AI infrastructure. The XE9680 excels as a flexible, multi-vendor GPU platform with exceptional expandability through 10 PCIe Gen5 slots and support for NVIDIA, AMD, and Intel accelerators. Its 4TB DDR5 system memory capacity and 122.88TB storage options make it well-suited for mixed workloads requiring substantial CPU processing alongside GPU acceleration. The 3+3 redundant power configuration and comprehensive management suite through iDRAC9 provide enterprise-grade reliability for data centre deployment.
The DGX B200 delivers unparalleled AI performance with its Blackwell GPUs, offering 72 PFLOPS FP8 training performance and 144 PFLOPS FP4 inference capability. The 14.4TB/s NVLink interconnect and 64TB/s GPU memory bandwidth represent significant architectural advantages for large-scale model training. However, this comes with less expansion flexibility and higher power requirements at approximately 14.3kW. The integrated NVIDIA software stack, including AI Enterprise and Mission Control, provides a turnkey solution but limits hardware customisation options.
Organisations should consider the XE9680 for heterogeneous AI environments requiring hardware flexibility, multi-vendor GPU support, and substantial expansion capabilities. The DGX B200 is better suited for organisations prioritising maximum AI performance with Blackwell GPUs, integrated networking through ConnectX-7 NICs, and a complete software ecosystem. The choice ultimately depends on whether operational flexibility or peak AI performance represents the higher priority for specific AI workloads.
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