H200 vs A100

The FP16 dense figure of 989 TFLOPS on the H200 compared with 312 TFLOPS on the A100 indicates the H200 can process larger batches during training and inference steps that rely on dense FP16 operations. The FP16 with sparsity figure of 1979 TFLOPS on the H200 versus 624 TFLOPS on the A100 shows the same relative advantage when sparsity acceleration is enabled. The FP32 rating of 67 TFLOPS on the H200 against 19.5 TFLOPS on the A100 further widens the gap for workloads that stay in single precision. Memory bandwidth of 4800 GB/s on the H200 compared with 2039 GB/s on the A100 allows larger batch sizes before memory stalls occur during both training and inference passes. The H200 INT8 dense rating of 1979 TOPS versus 624 TOPS on the A100 extends the same pattern to quantized inference paths. These deltas translate into fewer GPUs needed for a given workload size when the H200 is selected over the A100.

The H200 suits LLM training jobs that require the 141 GB HBM3e capacity to hold large model states without heavy partitioning. Its 4800 GB/s bandwidth also supports higher throughput on dense FP16 workloads measured at 989 TFLOPS compared with the A100.

The A100 fits deployments where the 400W TDP must stay within existing power envelopes that cannot accommodate the 700W TDP of the H200. Its established form factors remain compatible with older NVLink and PCIe 4.0 infrastructures already deployed.