Repeatable builds are not a requirement for determinism. Since the outputs can be determined based on the exact system running the code, it is deterministic - even though the output can vary based on the system running the code.

This is to say every output can be understood by understanding the systems that produced it. There are no dice rolls required. I.e. if it builds wrongly every other Tuesday, the reason for that can be determined (there's a line of code describing this logic).

While I don't disagree with your comment, I would say that a that a large language model, and a Docker build with a complex Dockerfile, where not every version is exactly pinned down, are quite similar. You might have updates from the base image, you might have updates from one of the thousands of dependencies. And each day you rebuild the image, you will get a different checksum. Similar to how you get different answers from the LLM. And just like you can get wrong answers from the LLM, you can also get Docker builds that start to behave differently over time.

So this is how it often is in practice. Then there is the possibility to pin down every version, and also some large language models support temperature 0. This is more in the realm of determinism.