⚡ NVIDIA DGX B300 vs DGX B200
NVIDIA DGX B300 vs NVIDIA DGX B200 compared across 23 matched specifications — scores, the full spec table and a verdict.


NVIDIA DGX B300 vs DGX B200: performance overview
Scores based on quantifiable specification values (1-10 scale)
Detailed Specifications
| Specification | NVIDIA DGX B300 NVIDIA DGX | NVIDIA DGX B200 NVIDIA DGX |
|---|---|---|
| Key Metrics | ||
| FP4 Inference Performance | 144 PFLOPS sparse / 108 PFLOPS dense | 144 PFLOPS sparse / 72 PFLOPS dense |
| FP8 Training Performance | 72 PFLOPS | 72 PFLOPS |
| Total GPU Memory | 2,304 GB HBM3e (8 × 288 GB) | 1,440 GB HBM3e |
| Network Port Speed | 800 Gb/s per port | 400 Gb/s per port |
| Form Factor | 10 RU rack-mount | 10 RU rack-mount |
| Compute | ||
| Processor | 2x Intel Xeon 6776P, 128 cores total (64 per CPU), 2.3 GHz base / 3.9 GHz max turbo | 2x Intel Xeon Platinum 8570, 112 cores, 2.1 / 4.0 GHz |
| GPU Configuration | 8x NVIDIA Blackwell Ultra GPUs | 8x NVIDIA Blackwell GPUs |
| GPU Interconnect | 2x NVLink Switch System — 14.4 TB/s aggregate | 2x NVLink Switch System — 14.4 TB/s aggregate |
| Memory | ||
| System Memory | Up to 4 TB DDR5 | 2 TB (configurable to 4 TB) |
| GPU Memory | 2,304 GB total HBM3e (8 × 288 GB) | 1.4 TB HBM3e — 64 TB/s bandwidth |
| Storage | ||
| OS Storage | 2x 1.9 TB NVMe M.2 | 2x 1.9 TB NVMe M.2 |
| Internal Storage | 8x 3.84 TB NVMe E1.S | 8x 3.84 TB NVMe U.2 |
| Networking | ||
| Network Interface Cards | 8x ConnectX-8 VPI OSFP (800 Gb/s) + 2x BlueField-3 DPU | 8x single-port ConnectX-7 VPI via 4x OSFP ports (up to 400 Gb/s) + 2x dual-port BlueField-3 DPU |
| GPU / Accelerators | ||
| GPU Memory Bandwidth | 62 TB/s | 64 TB/s |
| I/O & Ports | ||
| Network Ports | 8x OSFP (800 Gb/s) | 4x OSFP (serving 8x ConnectX-7, up to 400 Gb/s each) |
| Management | ||
| Management Software | NVIDIA Mission Control | NVIDIA Mission Control |
| Power | ||
| Maximum Power Consumption | 14.5 kW (busbar) / 15.1 kW (PSU version) | ~14.3 kW |
| Physical / Environmental | ||
| Dimensions | 442mm H × 482.6mm W × 904.2mm D | 444mm H × 482.2mm W × 897.1mm D |
| Cooling | Air-cooled chassis | Air-cooled chassis |
| System Weight | 168 kg (PSU version) / 123 kg (busbar version) | 142.4 kg max |
| Software & OS Compatibility | ||
| Operating System Support | NVIDIA DGX OS, Ubuntu, RHEL, Rocky Linux | NVIDIA DGX OS, Ubuntu, Red Hat Enterprise Linux, Rocky |
| Included Software | NVIDIA AI Enterprise, NVIDIA Mission Control, NVIDIA Run:ai | NVIDIA AI Enterprise, NVIDIA Mission Control, NVIDIA Run:ai |
| Warranty & Support | ||
| Support Period | 3-year business-standard hardware and software support | 3-year enterprise hardware and software support |
Expert Analysis
The NVIDIA DGX B300 and B200 represent two tiers within NVIDIA's Blackwell-generation AI supercomputing platform, sharing an 8-GPU, 10 RU design and 14.4 TB/s of NVLink bandwidth but differing in GPU (Blackwell Ultra vs Blackwell), host processors (Xeon 6776P vs Xeon Platinum 8570), GPU memory capacity and networking. The B300's primary advantage lies in its 2,304 GB of HBM3e GPU memory (8 × 288 GB)—60% more than the B200's 1,440 GB—making it particularly suited for massive model training where memory capacity directly determines model size and batch dimensions. This additional memory headroom enables the B300 to handle larger transformer models, more extensive context windows, and complex multi-modal AI workloads without requiring model parallelism or frequent checkpointing.
Network throughput represents the second key differentiator, with the B300 featuring 800 Gb/s ConnectX-8 NICs versus the B200's 400 Gb/s ConnectX-7. This doubling of per-port bandwidth significantly reduces communication bottlenecks in distributed training scenarios, particularly valuable for large-scale multi-node deployments where data parallelism across clusters demands high-speed interconnects. The B200 remains highly capable for single-node or smaller cluster deployments where 400 Gb/s networking provides sufficient bandwidth at a lower cost point.
Both systems are rated at 144 PFLOPS sparse FP4 inference and 72 PFLOPS sparse FP8 training, but NVIDIA quotes 1.5x the dense FP4 performance (108 vs 72 PFLOPS) and 2x the attention performance for the B300. Both share the same 14.4 TB/s NVLink bandwidth and include NVIDIA AI Enterprise and Mission Control. The choice between them hinges on workload characteristics: organisations training exceptionally large models or operating at massive scale will benefit from the B300's additional memory and networking headroom, while those with more moderate requirements may find the B200 delivers excellent performance at a more accessible price point. Infrastructure planning is not identical, though: both occupy 10 RU, but the B300 chassis is deeper (904.2 mm against 897.1 mm), draws 14.5 kW (busbar) or 15.1 kW (PSU version) against the B200's ~14.3 kW, and comes in either a 12-PSU AC version or a 54 V DC busbar version.
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