>And very rarely, this kind of thing might happen naturally.

It is not a question of rarity, it is a question of the stability of the numerical problem. Luckily most of the computation in an LLM is matrix multiplication, which is s extremely well understood numerical problem and which can be checked for good condition.

Two different numerical implementations on a well conditioned problem and which requires much computation, differing significantly would indicate a disastrous fault in the design or condition of the hardware, which would be noticed by most computations done on that hardware.

If you weigh the likelihood of OP running into a hardware bug, causing significant numerical error on one specific computational model against the alternative explanation of a problem in the software stack it is clear that the later explanation is orders of magnitude more likely. Finding a single floating point arithmetic hardware bug is exceedingly rare (although Intel had one), but stacking them up in a way in which one particular neural network does not function, while other functions on the hardware run perfectly fine, is astronomically unlikely.