[gf000]

I'm being deliberately pedantic, but depending on what kind of representation we use for the neural network (due to rounding) as well as the choice of inference (that is, given a distribution for next token, which one to choose), it can absolutely be reproducible and completely deterministic.

Though chaotic, which I believe is the better word here - a single letter change may result in widely different results.

We just choose to use more random inference rules, because they have better results.

[Neywiny]

With determinism you're not wrong. The problem is that you'd need to make sure all your seeds, temperatures, and other input parameters are exactly the same, and importantly that all context is cleared. But people don't do that. And I'm not sure every if even any provider lets you set those parameters.

[gf000]

Even with temperature set to zero, I believe due to FP operations not being commutative you may still get non-determinism, so what I am talking about (as mentioned, very pedantically) is mostly the theory.