B200 vs GH200

The B200's FP16 performance of 4500 TFLOPS vastly outpaces the GH200's 1979 TFLOPS, accelerating large-scale model training where FP16 precision dominates. This gap translates to roughly 2.3 times faster training iterations for deep learning tasks. In FP32, the B200's 90 TFLOPS edges out the GH200's 67 TFLOPS by 34 percent, benefiting simulations and general compute that require single-precision accuracy.

For inference, the B200's 9000 TFLOPS FP8 capability doubles the GH200's 3958 TFLOPS, enabling higher throughput for deploying quantized large language models. The doubled VRAM of 192 GB on the B200 supports batch sizes twice as large as the GH200's 96 GB limit, reducing per-token latency in production environments.

Memory bandwidth defines a key bottleneck: the B200's 8000 GB/s versus 4000 GB/s allows larger effective batch sizes without stalling, crucial for training massive transformers. The B200's 1000W TDP exceeds the GH200's 900W, demanding robust cooling, but yields proportional gains in sustained workloads.

The B200 excels in scenarios demanding maximum scale, such as training trillion-parameter LLMs that require 192 GB VRAM and 4500 TFLOPS FP16 throughput. Its 8000 GB/s bandwidth handles enormous datasets without bottlenecks, ideal for research labs pushing AI frontiers.

Enterprises prioritizing inference speed select the B200 for its 9000 TFLOPS FP8 performance, supporting high-volume serving of quantized models at lower latency than the GH200's 3958 TFLOPS.

Budget-sensitive users opt for the GH200 at $1.99 per hour, delivering solid 1979 TFLOPS FP16 for mid-scale training without the B200's $5.03 average cost. Its Grace CPU integration via NVLink-C2C enhances hybrid CPU-GPU workflows in scientific computing.

The GH200 suits inference for models fitting within 96 GB VRAM, where 3958 TFLOPS FP8 provides ample throughput at half the price of the B200.