A40 vs B200

The B200's FP16 performance of 4500 TFLOPS dwarfs the A40's 37.4 TFLOPS, offering approximately 120 times the throughput for deep learning training and inference where half-precision dominates. This delta accelerates model convergence: training a large language model on B200 completes in hours what takes days on A40. FP32 rates show B200 at 90 TFLOPS versus A40's 37.4 TFLOPS, benefiting scientific simulations requiring single-precision accuracy.

Memory specifications transform workload feasibility. The B200's 192 GB HBM3e VRAM supports batch sizes up to four times larger than A40's 48 GB GDDR6 limit, reducing overhead in inference pipelines. Its 8000 GB/s bandwidth versus 696 GB/s minimizes data transfer bottlenecks, enabling 11 times faster memory access for transformer models with extensive embeddings.

Power draw highlights trade-offs: A40's 300W TDP fits standard racks efficiently, while B200's 1000W demands advanced cooling. Interconnects favor B200 with NVLink, PCIe 6.0, and InfiniBand over A40's PCIe and NVLink alone, scaling multi-GPU clusters better for distributed training.

The A40 suits budget-conscious deployments in visualization, rendering, or legacy AI inference. At $0.24 per hour starting price and 300W TDP, it integrates into existing PCIe infrastructure without high power costs. Users with models under 48 GB VRAM benefit from its 37.4 TFLOPS FP16 for steady, cost-effective throughput across 22 cloud offers averaging $1.29 per hour.

Opt for the B200 in demanding AI training or large-scale inference requiring 192 GB VRAM and 4500 TFLOPS FP16. Its 8000 GB/s bandwidth handles massive batches efficiently, ideal for frontier models. Despite $1.71 per hour starting and 1000W TDP, the performance justifies costs in production environments with NVLink and InfiniBand scaling.