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.
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.
With batching matrix shapes/request position in them aren’t deterministic and this leads to non deterministic results, regardless of sampling temperature/seed.
If I had a black box api, just because you don't know how it's calculated doesn't mean that it's non-deterministic. It's the underlaying algorithm that determines that and a LLM is deterministic.
It’s inherently non deterministic because it reflects the reality of having different requests coming to the servers at the same time. And I don’t believe there are any realistic workarounds if you want to keep costs reasonable.
Edit: there might be workarounds if matmul algorithms will give stronger guarantees then they are today (invariance on rows/columns swap). Not an expert to say how feasible it is, especially in quantized scenario.
Every person in this thread understood that Simon meant "Grok, ChatGPT, and other common LLM interfaces run with a temperature>0 by default, and thus non-deterministically produce different outputs for the same query".
Sure, he wrote a shorter version of that, and because of that y'all can split hairs on the details ("yes it's correct for how most people interact with LLMs and for grok, but _technically_ it's not correct").
The point of English blog posts is not to be a long wall of logical prepositions, it's to convey ideas and information. The current wording seems fine to me.
The point of what he was saying was to caution readers "you might not get this if you try to repro it", and that is 100% correct.
Better phrasing would be something like "It's worth noting that LLM products are typically operated in a manner that produces non-deterministic output for the user"
Or you could abbreviate this by saying “LLMs are non-deterministic.” Yes, it requires some shared context with the audience to interpret correctly, but so does every text.