Claude estimates the average American consumes about 3800 liters of water per day counting all the food inputs (Claude only drank half a cup for this answer).
You did not count the gathering of data and then training the LLM.
Inference consumes far more total compute than training the model.
By 'inference', do you mean using the model to compute a specific series of results?
Yes, training is a tiny amount of usage, this is well-known.
Well, if that's the case, then the situation is worse than I imagined. I thought that training the model would be far more expensive computationally than using it to compute an 'answer'.
Thanks.
Training is much more computationally intensive than computing a _single_ answer. But not compared to running the model over time at scale for millions of users.
Thanks. That is precisely how I intuited the situation. 40+ years of programming FTW!