H200 vs RTX 5090

The FP16 dense figure of 989 TFLOPS on the H200 exceeds the 209.5 TFLOPS dense figure on the RTX 5090 by a factor derived directly from those values and this gap influences training throughput for precision formats that rely on dense operations. The FP32 performance of 104.8 TFLOPS on the RTX 5090 exceeds the 67 TFLOPS figure on the H200 and this difference affects workloads that depend on single precision arithmetic. Memory bandwidth of 4800 GB/s on the H200 compared with 1792 GB/s on the RTX 5090 determines feasible batch sizes during inference or training because higher bandwidth sustains larger data movements without stalls. The FP16 with sparsity figure of 1979 TFLOPS on the H200 compared with 419 TFLOPS on the RTX 5090 follows the same ratio pattern when sparsity is applied consistently.

The H200 suits LLM training tasks that require the full 141 GB HBM3e capacity and 4800 GB/s bandwidth to accommodate large model states. It also fits inference scenarios that benefit from the 1979 TFLOPS FP8 dense rating and NVLink interconnect for multi GPU scaling.

The RTX 5090 suits scientific computing tasks that utilize its 104.8 TFLOPS FP32 rating and 575W TDP. It also fits stable diffusion workloads where the 32 GB GDDR7 memory and PCIe 5.0 interconnect align with single GPU consumer deployments.