H200 vs RTX 5070

The FP16 dense performance of the H200 at 989 TFLOPS exceeds the RTX 5070 at 61.7 TFLOPS by a factor of 16. The FP32 performance shows the H200 at 67 TFLOPS compared to 30.9 TFLOPS on the RTX 5070. This delta means the H200 can process larger models during training and inference because its 4800 GB/s bandwidth supports bigger batch sizes than the 672 GB/s bandwidth on the RTX 5070. The H200 FP8 dense performance reaches 1979 TFLOPS while the RTX 5070 FP8 dense performance reaches 123.5 TFLOPS. The H200 FP16 with sparsity reaches 1979 TFLOPS while the RTX 5070 FP16 with sparsity reaches 123.5 TFLOPS. These ratios indicate the H200 sustains higher throughput for both dense and sparse workloads without mixing the two categories.

The H200 suits workloads that require 141 GB HBM3e VRAM and 4800 GB/s memory bandwidth for models exceeding 12 GB capacity. Its 989 TFLOPS dense FP16 and 67 TFLOPS FP32 enable faster completion of large training jobs compared to the RTX 5070 61.7 TFLOPS dense FP16 and 30.9 TFLOPS FP32. The 700W TDP and NVLink support fit multi GPU clusters handling scientific computing or full model training.

The RTX 5070 suits workloads that fit within 12-16 GB GDDR7 VRAM and operate under a 250W TDP limit. Its PCIe form factor and 672 GB/s bandwidth match consumer inference or fine tuning tasks where the H200 700W TDP and SXM form factor add unnecessary overhead. The 61.7 TFLOPS dense FP16 provides adequate speed for smaller Stable Diffusion jobs.