Is an LLM inherently deterministic?

What is this?
Exploring the determinism of large language models like GPT, and the possibility of designing them to be deterministic.?

Whether an LLM is inherently deterministic

The model is immutable at the point of use, so (use of a RNG or timing data notwithstanding) the output should be entirely deterministic? Not letting the caller pick a random seed seems to me to be a design choice.

Controlling all sources of entropy is hard if no-one has deliberately decided to try to make a process repeatable, but that doesn’t make the LLM non-deterministic.

(Back when I worked on a compiler, we carefully didn’t depend on any randomness – and on a single thread, the same input with the same compiler build should always give the same output. But change anything and all bets were off)

In response to the idea that GPT may be non-deterministic in design

I can easily believe it. That's where the design decision kicks in -- if they are happy allowing non-reproducible results, they don't need to spend the effort to make sure they don't introduce hard-to-remedy uncontrolled randomness.

Any source of true randomness may be replaced with pseudo-randomness, and any parallelism can be linearised. I'd be quite surprised if this weren't already in place for testing, although I may well be overestimating the utility of deterministic tests for LLMs or underestimating just how much of a performance regression I'm proposing.

I'm not saying it would necessarily be easy, cheap, or even worthwhile to expose a deterministic API -- but it would be possible. And I'd quite like one, even at a significantly higher per-call cost, if only because it makes it easier to argue that a large language model is not an AI.