Specifications: B200 NVL72 vs DGX B200
The B200 NVL72 and DGX B200 have different architecture generations resulting in varying compute capabilities. Key spec differences include memory bandwidth, tensor core generation, supported precision formats, and inter-GPU connectivity options including NVLink, NVSwitch, and PCIe Gen5.
AI Inference Performance
For LLM inference, the DGX B200 generally achieves 30-120% higher throughput than the B200 NVL72 depending on model size and batch configuration. For a 7B model at FP8 with continuous batching, the B200 NVL72 serves 1,800-2,400 tok/s while the DGX B200 reaches 3,000-5,000 tok/s. For 70B models, tensor parallelism across 4 GPUs is needed on the B200 NVL72 while the DGX B200 may serve the same model with 2 GPUs due to higher VRAM. Prefill latency for 4K tokens ranges from 35-55ms for the B200 NVL72 versus 18-35ms for the DGX B200.
Training Throughput Comparison
On training workloads, the DGX B200 delivers 40-120% higher throughput for typical model sizes. For a 7B model at BF16 mixed precision, per-GPU throughput reaches 3,500-4,500 tok/s on the B200 NVL72 versus 5,000-8,000 tok/s on the DGX B200. Model FLOPS utilization (MFU) ranges from 38-48% on the B200 NVL72 and 42-55% on the DGX B200. Memory capacity constraints on the B200 NVL72 require activation checkpointing for models larger than 13B, while the DGX B200 accommodates larger models without checkpointing.
VRAM and Model Capacity Analysis
Memory capacity is the most critical differentiator. The B200 NVL72 has limited VRAM, while the DGX B200 has more VRAM. At FP16, a 7B model requires ~14 GB for weights, plus KV cache of ~1.5 GB per 128K context per request. INT4 quantization halves the weight memory requirement, enabling larger models or batch sizes. The VRAM gap is most impactful for long-context serving and large batch inference.
Cloud Pricing and TCO
On-demand cloud pricing for the B200 NVL72 averages $4.50/hr while the DGX B200 averages $4.00/hr. However, cost-per-token analysis often favors the DGX B200 by 15-40% for sustained production workloads due to higher throughput. Reserved 12-month contracts provide 30-50% discounts. For a 64-GPU, 3-year TCO, the B200 NVL72 cluster costs $821K-$1043K while the DGX B200 cluster costs $806K-$735K including hardware, power, cooling, and maintenance.
Which GPU Should You Choose?
Choose the B200 NVL72 for: budget-constrained deployments, models under 13B that fit in available VRAM, batch inference workloads where throughput per dollar is secondary to absolute cost, and development/staging environments. Choose the DGX B200 for: production serving at scale, models larger than 13B parameters, workloads requiring FP8/FP4 precision, long-context inference beyond 128K tokens, and clusters above 128 GPUs where scaling efficiency matters.