OpenAI is deploying NVIDIA’s latest Blackwell B200 data center GPUs through the DGX B200 platform, a next-generation system designed for state-of-the-art AI training at massive scale. This article provides a technical overview of the DGX B200 system and its deployment considerations.
DGX B200 System Overview #
The NVIDIA DGX B200 is a high-performance AI compute node purpose-built for large language models, generative workloads, and advanced simulation tasks. It brings together high-bandwidth memory, dense networking, and the Blackwell architecture’s unprecedented compute capabilities.
DGX B200 System Specifications #
| Feature | Specification |
|---|---|
| GPU | NVIDIA Blackwell B200 Data Center GPUs |
| GPU Memory | 1.5 TB HBM3e Memory |
| System Memory (CPU) | 4 TB DRAM |
| Intra-System Interconnect | 8× NVLink-C200 (Total Bidirectional BW: 1.8 TB/s) |
| Inter-System Networking | 8× NVIDIA ConnectX-7 Network Adapters (NDRs) |
| Internal Storage | Up to 30 TB NVMe SSD |
| Dimensions | 10U height |
| Weight | ~300 kg |
⚡ Power Requirements for DGX B200 #
The DGX B200 requires robust power redundancy and stable high-density electrical planning:
- 6 Power Supply Modules (PSMs) per system
- Minimum 5/6 PSMs required for operation
- System remains online with 1 PSM failure
- System shuts down if ≥2 PSMs fail, regardless of remaining power availability
This design ensures predictable uptime and protects against under-voltage scenarios.
🌡️ Power and Cooling Planning #
| Feature | Value | Note |
|---|---|---|
| Thermal Design Power (TDP) | 12 kW | Typical operational load |
| Peak Power Requirement | 15 kW | For circuit capacity planning |
| Heat Dissipation | 12 kW | Cooling system must support this load |
| Airflow | 1200 CFM | Required for proper air cooling |
| Operating Temperature | 10°C – 35°C | Standard data-center conditions |
Circuit Deployment Recommendations #
Each rack should be powered by two independent electrical circuits, with each circuit capable of delivering 50% of the peak load. Proper breaker margins must be applied to accommodate peak-power transients.
Cooling Considerations #
Specialized cooling solutions such as rear-door heat exchangers or in-row coolers are generally not recommended due to the DGX B200’s very high heat density and airflow requirements.
🌐 DGX SuperPOD Architecture #
The NVIDIA DGX SuperPOD integrates multiple DGX B200 systems into a unified, scalable AI compute cluster.
- Standard Deployment:
A 48U–52U rack can host two air-cooled DGX B200 units.
-
High-Density Deployment:
A 52U rack can house up to four DGX B200 units for environments optimized for maximum compute per rack. -
Inter-System Networking:
Uses InfiniBand (IB) for high-bandwidth, low-latency communication across systems. -
Cabling Requirements:
IB architecture determines rack-to-rack distance and cable length planning. -
Max Scale:
A DGX SuperPOD can scale to 127 DGX B200 systems, grouped into clusters of 32 nodes for topology efficiency.
