B200 SXM vs H200 SXM

The B200 SXM outperforms in compute-intensive tasks: its 4500 TFLOPS FP16 rate, more than double the H200's 1979 TFLOPS, accelerates LLM training by processing tensor operations faster. FP32 performance of 90 TFLOPS versus 67 TFLOPS benefits scientific simulations requiring single-precision accuracy. FP8 throughput at 9000 TFLOPS on B200 doubles the H200's 3958 TFLOPS, optimizing quantized inference for deployment.

Memory specifications prove decisive for real-world use: 192 GB HBM3e VRAM on B200 supports larger models or batch sizes than the H200's 141 GB, avoiding out-of-memory errors in fine-tuning. Bandwidth of 8000 GB/s, 67 percent higher than 4800 GB/s, sustains high throughput during data-heavy inference, reducing bottlenecks in large-batch processing. The B200's 1000W TDP reflects this power for peak performance, contrasting the H200's 700W efficiency.

Select the B200 SXM for workloads demanding maximum throughput, such as training massive LLMs where 4500 TFLOPS FP16 and 192 GB VRAM enable handling models over 141 GB without partitioning. Its 8000 GB/s bandwidth supports large-batch training, cutting iteration times. PCIe 6.0 and NVLink interconnects future-proof multi-GPU clusters.

High FP8 performance at 9000 TFLOPS suits quantized inference at scale, justifying $1.71 per hour starting price for performance-critical applications.

The H200 SXM fits cost-sensitive deployments: pricing from $1.19 per hour (average $3.81 per hour across 22 offers) undercuts B200's $1.71 per hour. Lower 700W TDP reduces cooling and power costs in dense clusters.

It suffices for inference or fine-tuning under 141 GB VRAM, with 3958 TFLOPS FP8 delivering efficient quantized serving where B200's extras yield diminishing returns.