Home Comparisons H100 SXM vs A100 SXM4 H100 or A100: which to pick for AI and training Both are data-centre cards with 80 GB of memory. H100 is built on Hopper and supports FP8, which speeds up training and inference of large transformers. A100 on Ampere is a mature card and noticeably cheaper for work that does not need FP8.
Specifications Prices and availability come from the live catalog and update automatically.
Which one to pick Pick H100 SXM if training and fine-tuning large models high-throughput inference FP8 and the Transformer Engine Pick A100 SXM4 if budget fine-tuning up to 13B work without FP8 and with moderate load a stable pick for classic ML pipelines Cost example H100 SXM, per hour: about 306 ₽ A100 SXM4, per hour: about 172 ₽ Estimate at the current per-GPU hourly price. Billed per minute, the price is fixed at launch.
Questions and answers
Which is faster for LLMs, H100 or A100? H100 is usually much faster on transformers, especially with FP8. The exact gain depends on the model and framework; measure on your own task.
Which card should I use for a 70B model? For inference, a 70B model at int4 fits one 80 GB card. Training needs several cards, and then the link between them matters.