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> It’s worth noting that LLMs are non-deterministic,

This is probably better phrased as "LLMs may not provide consistent answers due to changing data and built-in randomness."

Barring rare(?) GPU race conditions, LLMs produce the same output given the same inputs.

I run my local LLMs with a seed of one. If I re-run my "ai" command (which starts a conversation with its parameters as a prompt) I get exactly the same output every single time.

In my (poor) understanding, this can depend on hardware details. What are you running your models on? I haven't paid close attention to this with LLMs, but I've tried very hard to get non-deterministic behavior out of my training runs for other kinds of transformer models and was never able to on my 2080, 4090, or an A100. PyTorch docs have a note saying that in general it's impossible: https://docs.pytorch.org/docs/stable/notes/randomness.html

Inference on a generic LLM may not be subject to these non-determinisms even on a GPU though, idk