Specifications: GB200 NVL72 vs GB300 NVL72
The GB200 NVL72 and GB300 NVL72 represent different generations of GPU architecture for AI workloads. Memory capacity ranges from NVL72GB to NVL72GB, with significant differences in memory bandwidth, compute throughput (FP8/FP16/TF32), NVLink connectivity, and TDP. The GB300 NVL72 supports newer technologies like FP8 transformer engines, fourth-gen tensor cores, and higher-bandwidth HBM3e or HBM4 memory.
AI Inference Performance
For LLM inference, the GB300 NVL72 generally achieves 30-120% higher throughput than the GB200 NVL72 depending on model size and batch configuration. For a 7B model at FP8 with continuous batching, the GB200 NVL72 serves 1,800-2,400 tok/s while the GB300 NVL72 reaches 3,000-5,000 tok/s. For 70B models, tensor parallelism across 4 GPUs is needed on the GB200 NVL72 while the GB300 NVL72 may serve the same model with 2 GPUs due to higher VRAM. Prefill latency for 4K tokens ranges from 35-55ms for the GB200 NVL72 versus 18-35ms for the GB300 NVL72.
Training Throughput Comparison
On training workloads, the GB300 NVL72 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 GB200 NVL72 versus 5,000-8,000 tok/s on the GB300 NVL72. Model FLOPS utilization (MFU) ranges from 38-48% on the GB200 NVL72 and 42-55% on the GB300 NVL72. Memory capacity constraints on the GB200 NVL72 require activation checkpointing for models larger than 13B, while the GB300 NVL72 accommodates larger models without checkpointing.
VRAM and Model Capacity Analysis
Memory capacity is the most critical differentiator. The GB200 NVL72 has NVL72 VRAM, while the GB300 NVL72 has NVL72 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 GB200 NVL72 averages $4.50/hr while the GB300 NVL72 averages $6.00/hr. However, cost-per-token analysis often favors the GB300 NVL72 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 GB200 NVL72 cluster costs $558K-$687K while the GB300 NVL72 cluster costs $790K-$1737K including hardware, power, cooling, and maintenance.
Which GPU Should You Choose?
Choose the GB200 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 GB300 NVL72 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.