That's not inherent, that's a consequence of performance optimizations. It's absolutely a choice to run those matrix calculations in a way that fails to have predictable execution ordering. It's just that the speed benefits to allowing that are considerable.
You can make it trivially deterministic by running single threaded on a cpu, but it's becomes too slow for practical applications if you do that.
well sure, but i mean realistically speaking, we cannot step debug an llm's output to find out what happened given the way we currently execute inference
Depends on who "we" are, what you're talking about is a thing for inference providers doing batched inference and similar stuff. If you run one inference requests locally, you can actually step-by-step debug LLM output, just there is a ton of steps. But there is nothing "inherently random" or non-deterministic involved here, just optimization strategies for the large inference servers that makes it "impossible".
> we cannot step debug an llm's output to find out what happened
We absolutely can with mechanistic interpretability & companies like Anthropic, OpenAI, Meta, and Google do precisely this do debug their models.