H200 NVL vs RTX 5080

The H200 NVL dominates in AI accelerators due to its FP16 throughput of 1979 TFLOPS and FP8 at 3958 TFLOPS, enabling rapid large-scale model training and quantized inference. RTX 5080 matches FP32 at 56.3 TFLOPS with identical FP16, suiting graphics rendering where single-precision matters equally. This FP16 to FP32 delta on H200 favors deep learning pipelines: training benefits from 1979 TFLOPS half-precision speedups, while inference leverages FP8 for models exceeding 16 GB VRAM. Memory bandwidth of 4800 GB/s on H200 supports batch sizes up to eight times larger than RTX 5080's 960 GB/s, reducing latency in transformer models. In practice, H200 handles 141 GB datasets without swapping, whereas RTX 5080 limits to 16 GB, constraining complex simulations. Power draw reflects intent: 700W TDP for H200 sustains peak loads, against 360W for efficient edge tasks.

Opt for the H200 NVL in scenarios demanding extreme scale, such as training LLMs with billions of parameters requiring 141 GB HBM3e VRAM. Its 4800 GB/s bandwidth and NVLink interconnect excel in multi-GPU clusters for distributed training, where RTX 5080's 16 GB GDDR7 falls short. Cloud deployments at $0.50 per hour justify costs for production inference at 3958 TFLOPS FP8.

Select the RTX 5080 for cost-sensitive graphics or small-scale AI, where 56.3 TFLOPS FP32 aligns with gaming or Stable Diffusion at $0.25 per hour. Its 360W TDP and PCIe form factor suit single-node workstations avoiding datacenter overhead. Bandwidth of 960 GB/s handles batch sizes fitting 16 GB VRAM efficiently.