⚡ Supermicro SYS-821GE vs NVIDIA DGX B300
AI-powered analysis across 24 matched specifications
Performance Overview
Scores based on quantifiable specification values (1-10 scale)
Detailed Specifications
| Specification | SYS-821GE-TNHR Supermicro | NVIDIA DGX B300 NVIDIA DGX |
|---|---|---|
| Key Metrics | ||
| Form Factor | 8U Rackmount (CSE-GP801TS chassis) | 10 RU (444mm H × 482.2mm W × 897.1mm D) |
| GPU Configuration | 8× NVIDIA HGX H100 8-GPU (80GB HBM2e) or HGX H200 8-GPU (141GB HBM3e); SXM form factor | 8x NVIDIA Blackwell Ultra GPUs |
| Total GPU Memory | Up to 1.128TB (H200) / 640GB (H100) | 2.3 TB total HBM3e |
| AI Performance (FP8 Training) | Not specified | 72 PFLOPS (with sparsity) |
| AI Performance (FP4 Inference) | Not specified | 144 PFLOPS (with sparsity) |
| Compute | ||
| Processor | Dual Socket E (LGA-4677) — 5th/4th Gen Intel Xeon Scalable; up to 64C/128T, 320MB LLC, 350W TDP | Intel Xeon 6776P, 112 cores, up to 4 GHz boost |
| GPU Interconnect | NVIDIA NVLink 4.0 + NVSwitch — 900 GB/s all-to-all bandwidth | 2x NVLink Switch System — 14.4 TB/s aggregate |
| CPU-GPU Interconnect | PCIe 5.0 x16 | -- |
| Memory | ||
| Memory | 32× DDR5 DIMM slots; up to 8TB ECC RDIMM at 5600MT/s | Up to 4 TB DDR5 |
| Storage | ||
| Storage Options | 12× front hot-swap 2.5" NVMe (default); optional 4× additional NVMe; 3× 2.5" SATA; 2× M.2 NVMe | OS: 2x 1.9 TB NVMe M.2; Internal: 8x 3.84 TB NVMe E1.S |
| Networking | ||
| Networking | Flexible — PCIe 5.0 slots support 400GbE / NDR InfiniBand NICs | 8x ConnectX-8 VPI OSFP (800 Gb/s) + 2x BlueField-3 DPU |
| GPU / Accelerators | ||
| GPU Memory Per GPU | 80GB HBM2e (H100) / 141GB HBM3e (H200) | 287.5GB HBM3e (average per GPU) |
| Expansion / PCIe | ||
| PCIe Slots | 8× PCIe 5.0 x16 LP + 2× PCIe 5.0 x16 FHHL (optional: 2 additional FHHL) | -- |
| I/O & Ports | ||
| Management Port | Dedicated BMC port | -- |
| Management | ||
| Management | IPMI 2.0, dedicated BMC port, SuperDoctor; ACPI power management | NVIDIA Mission Control |
| Power | ||
| Power Supply | 6× 3,000W redundant (4+2) Titanium 96% efficiency | ~14 kW max |
| Physical / Environmental | ||
| Cooling | Air-cooled — 10 heavy-duty fans with PWM speed control; no CDU required | -- |
| Operating Temperature | 10°C to 35°C (50°F to 95°F) | -- |
| Weight | Net: 75.3 kg (166 lbs); Gross: 102.1 kg (225 lbs) | -- |
| Dimensions | 29.5"H × 27.5"W × 51.2"D | 444mm H × 482.2mm W × 897.1mm D |
| Security | ||
| Security | Silicon RoT (NIST 800-193), Secure Boot, TPM header, cryptographically signed firmware | -- |
| Software & OS Compatibility | ||
| OS Support | -- | NVIDIA DGX OS, Ubuntu, RHEL, Rocky Linux |
| Software | -- | NVIDIA AI Enterprise, NVIDIA Mission Control, NVIDIA Run:ai |
| Warranty & Support | ||
| Support | -- | 3-year business-standard hardware and software support |
Expert Analysis
The Supermicro SYS-821GE-TNHR and NVIDIA DGX B300 represent two distinct approaches to high-performance AI infrastructure. The Supermicro system offers exceptional flexibility with its modular design, supporting both H100 and H200 GPU configurations, 8TB of system memory across 32 DDR5 DIMMs, and extensive PCIe 5.0 expansion capabilities. Its air-cooled 8U design makes it suitable for data centres without liquid cooling infrastructure, while the comprehensive security features and IPMI management provide robust enterprise control. However, it requires separate networking and software stack procurement.
The NVIDIA DGX B300 delivers a fully integrated, performance-optimised solution with the latest Blackwell Ultra GPUs offering 2.3TB total HBM3e memory and groundbreaking 144 PFLOPS FP4 inference performance. The 14.4 TB/s NVLink bandwidth creates a unified GPU domain, while the integrated 800Gb/s networking and BlueField-3 DPUs provide exceptional data throughput. The included NVIDIA AI Enterprise software stack and three-year support package reduce deployment complexity, though the 10RU form factor and fixed configuration limit customisation options.
For organisations requiring maximum flexibility, custom configurations, and gradual GPU upgrades, the Supermicro system provides superior expandability and choice. The NVIDIA DGX B300 excels in environments prioritising out-of-the-box performance, integrated software ecosystems, and simplified deployment for large-scale AI training and inference workloads where maximum GPU-to-GPU bandwidth is critical.
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