B200 vs H200

The B200's FP16 performance of 4500 TFLOPS dwarfs the H200's 1979 TFLOPS, accelerating large model training by handling more floating-point operations per second. FP32 rates show 90 TFLOPS for B200 against 67 TFLOPS for H200, benefiting precision-sensitive tasks like scientific simulations. In real-world training, this delta reduces epochs needed for convergence on massive datasets.

FP8 performance reaches 9000 TFLOPS on B200 versus 3958 TFLOPS on H200, optimizing inference for quantized models with lower precision needs. Higher memory bandwidth of 8000 GB/s on B200 supports larger batch sizes than the H200's 4800 GB/s, minimizing data loading bottlenecks in transformer models. This enables training with bigger effective batch sizes, improving throughput and stability.

Power draw differs at 1000W TDP for B200 compared to 700W for H200, influencing cooling and cluster density. PCIe 6.0 on B200 outpaces H200's PCIe 5.0, enhancing multi-GPU scaling in NVLink setups.

The B200 excels in scenarios demanding maximum throughput, such as training LLMs exceeding 100 billion parameters, where 192 GB VRAM and 8000 GB/s bandwidth handle vast datasets without swapping. Its 4500 TFLOPS FP16 performance cuts training time significantly over H200's 1979 TFLOPS.

Researchers tackling frontier models or inference at scale select B200 for FP8 efficiency at 9000 TFLOPS, despite $4.89 per hour starting price.

The H200 suits cost-sensitive deployments, with pricing from $0.49 per hour versus B200's $4.89, ideal for inference serving or fine-tuning where 141 GB VRAM suffices. Lower 700W TDP reduces operational costs in dense clusters.

Teams prototyping or running production inference on mid-sized models favor H200's 3958 TFLOPS FP8 performance across more providers.