> Naively, I would assume it must perform its normal auto regressive decoding to know what the “correct” token is in order to have something to compare the candidate token with.

Yes, but you can do it in parallel.

Suppose you predicted the tokens "D E F" in the sequence "A B C D E F". To "generate" the last token (F), it must know all preceding tokens (A B C D E). To "generate" the next-to-last token (E), it must know all preceding tokens (A B C D). And so on.

Assuming the prediction is correct, it can then run the "generation" for tokens D, E, and F at the same time. At the end, after all these tokens were "generated", it compares each token with the prediction; if the "generation" result was "D H F" it knows it has to discard the last two predicted tokens (and output "D H"), if the "generation" was "D E H" it knows it has to discard the last predicted token (and output "D E H"), etc.

And the most important part is that you can do it in parallel for each layer of the model. That is, you run "A B C D E F" through the first layer, then through the second layer, and so on; you only have to load the model weights from memory once for each layer. Instead of reading the full weights for all layers once for D, then once for E, then once for F, you only read them once for "D E F", and if the prediction was correct, you output three tokens by the (memory read) price of one (you still had to do the same amount of compute, but AFAIK LLMs tend to be more memory-bound than compute-bound).