H100 SXM5 vs RTX 5090

The H100 SXM5's FP16 throughput of 1979 TFLOPS vastly exceeds the RTX 5090's 419 TFLOPS, accelerating deep learning training and inference where half-precision dominates. This gap translates to faster convergence in model training cycles and higher inference throughput for large language models. In FP32 workloads, the RTX 5090's 105 TFLOPS surpasses the H100's 67 TFLOPS, benefiting scientific simulations or rendering that rely on single-precision arithmetic.

Memory differences prove critical: the H100's 3350 GB/s bandwidth and 80 to 94 GB VRAM support massive batch sizes, enabling efficient training of models with billions of parameters on single GPUs. The RTX 5090's 1792 GB/s and 32 GB limit it to smaller batches or models, potentially requiring multi-GPU scaling for comparable tasks. FP8 performance follows suit, with H100 at 3958 TFLOPS versus 838 TFLOPS, favoring quantized inference on the enterprise card.

Select the H100 SXM5 for large-scale LLM training or inference involving models exceeding 32 GB VRAM, such as those with hundreds of billions of parameters. Its 80 to 94 GB HBM3 and 3350 GB/s bandwidth handle enormous datasets and batch sizes without fragmentation. Datacenter features like NVLink and 700W TDP optimize multi-GPU clusters despite higher $3.50 per hour average cost.

Opt for the RTX 5090 in cost-sensitive scenarios like fine-tuning mid-sized models or Stable Diffusion generation, where 32 GB GDDR7 suffices. Its lower $0.63 per hour average and 575W TDP reduce operational expenses for single-user workflows. Superior FP32 at 105 TFLOPS suits graphics-intensive tasks or smaller inference deployments.