Are you arguing that the output of an LLM isn’t random?
It is not random if you don't use random sampling in its output generation.
It the whole thing were actually stochastic then prompt caching would be impossible because having a cache of what the previous tokens transformed into to speed up future generation would be invalidated by the missing random state.
Look at llama.cpp, you can see what samplers are adjustable and if you use samplers that employ randomness you can see what settings disable the random sampling. Or you can employ randomness but fix the seed to get reproducible results.
https://docs.pytorch.org/docs/2.11/generated/torch.use_deter...
This comes down to map reduce and floating point's lack of associativity. You see the same thing with OpenMP on CPUs.
People are constantly claiming determinism in LLMs that is just not there.
This is interesting though, I didn't know PyTorch had a debug mode for reproducibility.
I posted that to show that at a bare minimum, there is some pretty extreme nondeterminism (though probably mild in effect) in even the most pedestrian GPU inference, unless you go to the extreme of using the debug mode and taking the potential performance hit.
ill conditioned or unstable is better
a small change in the input can create a large difference in the output.