B200 NVL vs H100 NVL

The B200 NVL excels in raw compute: its 4500 TFLOPS FP16 throughput doubles the H100 NVL's 1979 TFLOPS, accelerating large model training where tensor core utilization peaks. FP32 performance edges forward at 90 TFLOPS versus 67 TFLOPS, benefiting simulation-heavy tasks. For inference, FP8 dominance shines with 9000 TFLOPS on B200 against 3958 TFLOPS on H100, enabling higher throughput for quantized LLMs. Memory specs transform workflows: 192 GB HBM3e VRAM on B200 supports models exceeding 100 billion parameters without sharding, unlike H100's 80-94 GB limit. Bandwidth of 8000 GB/s versus 3350 GB/s permits larger batch sizes, reducing iteration times in training by minimizing data movement bottlenecks. Power draw rises to 1000W TDP from 700W, demanding robust cooling but yielding efficiency gains per watt in dense NVL setups. Interconnects advance to PCIe 6.0 on B200 from PCIe 5.0, enhancing multi-node scaling.

Opt for the B200 NVL when tackling the largest AI models: its 192 GB HBM3e VRAM handles unfragmented 1 trillion parameter training, impossible on H100 NVL's 80-94 GB. Scenarios demanding peak FP8 inference at 9000 TFLOPS suit enterprise serving massive LLMs. Future deployments benefit from Blackwell's 2024 architecture and 8000 GB/s bandwidth for sustained high-batch workloads.

Select the H100 NVL for cost-effective scaling: pricing from $1.40 per hour across nine providers contrasts B200 NVL's $10.50 per hour scarcity. Mature Hopper ecosystem supports immediate deployment in fine-tuning or inference at 3958 TFLOPS FP8. Lower 700W TDP eases integration into existing clusters with PCIe 5.0 and NVLink.