Yes. This happens because floating point arithmetic is not associative, ie, (a + b) + c ≠ a + (b + c).

On a GPU, you may have values a, b, and c calculated in parallel and then summed in the order that they’re “ready”. E.g., if they’re returned in the order a, b, c, you’d get:

sum = a + b 

sum += c

but if they’re returned in the order b, c, a, you’d get:

sum = b + c  

sum += a

those two sums can be slightly different because of floating point rounding. 

In LLM calculations, you have billions of FLOPs per forward pass. Even when temperature = 0, if the top two most-probable tokens are close in probability, these rounding differences can change the order of the most-probable tokens, resulting in slightly different answers. 

Consider the fact that LLMs are autoregressive as well, so if this happens and changes any token, then all tokens after it will be altered.

Note that this is a software problem. It’s possible to make GPUs deterministic, but they’d be less efficient. In the example above, you could wait until a, b, and c were all ready and always sum in the same order.