>Let us consider the GPT-3 model with 𝑃 =175 billion parameters as an example. This model was trained on 𝑇 = 300 billion tokens. On 𝑛 = 1024 A100 GPUs using batch-size 1536, we achieve 𝑋 = 140 teraFLOP/s per GPU. As a result, the time required to train this model is 34 days.

https://arxiv.org/pdf/2104.04473.pdf

I'm not sure expressing brain capacity in FLOPs makes much sense, but I'm sure if it can be expressed in FLOPs, the amount of FLOPs going to learning for a normal human is less than that.