They are: https://openai.com/index/cerebras-partnership/ My guess is they only consider Luna "good enough" to justify the immense up-front investment to put it onto silicon, but Luna at 10x the current speed would be killer. If they're really pursuing live voice conversations with a hardware assistant, latency is more important than accuracy (for complex questions the assistant could always say something like "wait a minute, I need to think about this" and hand over to another model).
Cerebras doesn't etch the model onto silicon though. They're basically just wafer scale GPUs. They're more flexible than etched silicon though because they can just run the next version of the model almost straight away.
Interesting. I did not know that. And still they got 15x speedup (https://www.cerebras.ai/blog/openai-gpt-oss-120b-runs-fastes...). I wonder how much additional speedup would be possible by really etching the model.
How ? Do LLMs actually "know' when they don't "know" ?
They do this all the time, I'm using ChatGPT in Instant mode and it auto updates to thinking if my question is complex. Most of the time this works.
To answer your question: A large language model itself does not know this (afaik). But chatbots are not "just LLMs" but a whole bunch of systems (and models) around them.
Ok, but the article is about etching the model, not a "whole bunch of systems". So far, I still don't if its actually doable or if it is just unsubstantiated speculation.
How do humans?
Always the same trick of not answering the question and deflecting to „what about humans“. Can you folks not evaluate LLMs as the system they are, without vague gestures at how a different system behaves?
Evaluating LLMs is incredibly difficult. They are categorically different from any other system we have intuition about.
That said, I read the question I am replying to as a rhetorical one. If it was meant as a genuine question, curious about the question of meta knowledge, then I misread. Certainly the question is extremely interesting, for both LLMs and humans! But it's also obviously a very difficult one, as we don't even have a clear theory on how "knowing" works in the base case.