this post was submitted on 20 Jul 2023
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That's not how these LLMs work. There is a training phase which takes a large amount of compute power, and the training generates a model which is a set of weights and could easily be backed up and version-controlled. The model is then used for inference which is a less compute-intensive process and runs on much smaller hardware than the training phase.
The inference architecture does use feedback mechanisms but the feedback does not modify the model-weights that were generated at training time.
They list the currently available models that users of their API can select here:
https://platform.openai.com/docs/models/overview
They even say that while the main models are being continuously updated (read: re-trained) there are snapshots of previous models that will remain static.
So yes, they are storing and snapshotting the models and they have many different models available with which to perform inference at the same time.
Each parameter corresponds to a single number, so if it’s using 16 bit numbers then that’s 200 TB. They might be using 32 bit numbers (400 TB) but wouldn’t be using anything larger.
Makes me wonder how exactly they curate said data, its such an insane amount even teams of thousands of human programmers sifting through all of it 24/7 all day everyday wouldn't be able to fact check or assess all the data for years. Presumably they use ai to go over the data scraped and thrown into the model, since I cant imagine any human being able to curate it all.
I've heard from various videos detailing the topic that many of the developers have little to no clue as to what's going on inside the LLM once it's assembled and set about its work on training itself and what not- and I'm inclined to believe them, the human programmers simply set the params, and system up and then the system eats all the data loaded into it and immediately becomes a sort of black box which nobody knows exactly whats going on inside of it to produce the output it does.