H200 vs RTX 5080

The H200's 141 GB HBM3e VRAM supports model sizes and batch sizes unattainable on the RTX 5080's 16 GB GDDR7, such as loading 70B parameter LLMs without sharding. This VRAM disparity proves critical for training where datasets exceed 16 GB, allowing the H200 to process larger batches efficiently. Memory bandwidth of 4800 GB/s on the H200 versus 960 GB/s on the RTX 5080 accelerates data movement, reducing bottlenecks in memory-bound operations like transformer attention mechanisms.

FP16 performance on the H200 at 1979 TFLOPS vastly outpaces the RTX 5080's 56.3 TFLOPS, favoring mixed-precision training where speedups reach over 35 times in theoretical throughput. The H200's FP32 at 67 TFLOPS slightly edges the RTX 5080's 56.3 TFLOPS, benefiting simulations requiring full precision. For inference, the H200's FP8 capability of 3958 TFLOPS enables quantized deployments at scales impossible on the RTX 5080, though the latter's balanced FP16 and FP32 suit graphics rasterization.

Power consumption highlights trade-offs: the H200's 700W TDP demands robust cooling and infrastructure, while the RTX 5080's 360W fits standard PCIe setups, lowering operational costs in smaller clusters.

The H200 excels in large-scale AI training and inference where 141 GB VRAM handles massive models like 175B parameters without multi-GPU complexity. Its 4800 GB/s bandwidth and 1979 TFLOPS FP16 performance support high-throughput workloads in datacenters, ideal for enterprises running on NVLink or InfiniBand interconnects. Cloud users prioritizing raw compute over cost select H200 for tasks demanding FP8 at 3958 TFLOPS.

The RTX 5080 suits budget-conscious users with 16 GB GDDR7 VRAM for Stable Diffusion or fine-tuning smaller models under 7B parameters. At $0.25 per hour average, it offers 56.3 TFLOPS FP32 for graphics and gaming, fitting PCIe form factors with 360W TDP. Prototyping or edge inference favors its Blackwell efficiency where H200's scale proves overkill.