Application-specific AI models can be much smaller and faster than the general purpose, do-everything LLM models. This allows them to run locally.

They can also be made to be deterministic. Some extra care is required to avoid computation paths that lead to numerical differences on different machines, but this can be accomplished reliably with small models that use integer math and use kernels that follow a specific order of operations. You get a lot more freedom to do these things on the small, application-specific models than you do when you're trying to run a big LLM across different GPU implementations in floating point.

Some people care more about compile times than the performance of generated code. Perhaps even the correctness of generated code. Perhaps more so than determinism of the generated code. Different people in different contexts can have different priorities. Trying to make everyone happy can sometimes lead to making no one happy. Thus dichotomies like `-O2` vs `-Os`.

EDIT (since HN is preventing me from responding):

> Some people care more about compiler speed than the correctness?

Yeah, I think plenty of people writing code in languages that have concepts like Undefined Behavior technically don't really care as much about correctness as they may claim otherwise, as it's pretty hard to write large volumes of code without indirectly relying on UB somewhere. What is correct in such case was left up to interpretation of the implementer by ISO WG14.