If you use a deterministic sampling strategy for the next token (e.g., always output the token with the highest probability) then a traditional LLM should be deterministic on the same hardware/software stack.
Deterministic is one thing, but stable to small perturbations in the input is another.
Wouldn't seeding the RNG used to pick the next token be more configurable? How would changing the hardware/other software make a difference to what comes out of the model?