Hi,
Looking at the model repo 1, it seems to be 41.5 GB. Actually you need twice as much CPU RAM in order to load the model. When calling .from_pretrained(), the model actually gets loaded twice: once with randomly initialized weights, once with the pretrained weights. However, @stas has added a new (experimental) argument called low_cpu_mem_usage, which can be set to True, in order to only load the model once into CPU memory (directly with the pretrained weights), see this PR 2. So using that argument, it requires at least 41.5 GB of CPU RAM.
Next, if you want to perform inference on GPU, you also need at least the same amount of GPU RAM (41.5 GB) in order to put the model on it, + you need some extra space for the data you put on it, as well as the activations (i.e. logits).