H100 vs RTX 5070

Dense FP16 performance reaches 989 TFLOPS on the H100 compared to 61.7 TFLOPS dense FP16 on the RTX 5070. This difference means the H100 handles training and inference for larger neural networks at greater speeds. The FP32 rating of 67 TFLOPS on the H100 versus 30.9 TFLOPS on the RTX 5070 reinforces advantages in precision sensitive tasks. Memory bandwidth of 3350 GB per s on the H100 allows for increased batch sizes during model execution relative to 672 GB per s on the RTX 5070. With sparsity the H100 attains 1979 TFLOPS in FP16 while the RTX 5070 reaches 123.5 TFLOPS with sparsity in FP16. These figures illustrate how the H100 scales better for intensive tensor operations.

The H100 serves as the better choice for scenarios involving models that exceed 16 GB of memory requirements. Its 80 to 94 GB VRAM capacity and 3350 GB per s bandwidth support large batch processing in scientific computing applications. The 700 W TDP accommodates data center installations where power delivery is available.

The RTX 5070 fits use cases with power limited environments or smaller model sizes under 12 GB. Its 250 W TDP and PCIe form factor enable deployment in standard workstations for tasks such as stable diffusion at reduced energy consumption.