H100 vs RTX 3090

The FP16 dense performance of 989 TFLOPS on the H100 compared to 71 TFLOPS on the RTX 3090 indicates substantial differences in training and inference speeds for models using half precision. When sparsity is applied the H100 reaches 1979 TFLOPS against 142 TFLOPS on the RTX 3090. The FP32 performance at 67 TFLOPS versus 35.6 TFLOPS further highlights advantages in single precision tasks. Memory bandwidth of 3350 GB/s versus 936 GB/s allows larger batch sizes on the H100 during operations. The INT8 dense performance stands at 1979 TOPS for the H100 compared to 284 TOPS for the RTX 3090. These ratios demonstrate how the H100 supports more intensive tensor workloads than the RTX 3090 across matching precision categories.

The H100 suits scenarios that demand high memory capacity such as 80 to 94 GB HBM3 for large scale model handling. Its FP16 dense performance of 989 TFLOPS and memory bandwidth of 3350 GB/s enable efficient processing of extensive datasets. The 700W TDP pairs with interconnect options including NVLink for multi GPU setups in advanced compute environments.

The RTX 3090 fits tasks with moderate requirements where 24 GB GDDR6X suffices and power draw remains at 350W. Its FP32 performance of 35.6 TFLOPS supports standard inference and training without excess capacity. The PCIe form factor and 936 GB/s bandwidth align with compact deployments focused on efficiency.