"We know the number of floating point operations per token for inference is approximately twice the number of parameters"

Does someone have a source for this?

(By the way, it is unknown how many parameters GPT-3.5 has, the foundation model which powers finetuned models like ChatGPT and text-davinci-003. GPT-3 had 175 billion parameters, but per the Hoffmann et al Chinchilla paper it wasn't trained compute efficiently, i.e. it had too many parameters relative to its amount of training data. It seems likely that GPT-3.5 was trained on more data with fewer parameters, similar to Chinchilla. GPT-3: 175B parameters, 300B tokens; Chinchilla: 70B parameters, 1.4T tokens.)