alt Hacker NewsNPUs are still useful for LLM pre-processing and other compute-bound tasks. They will waste memory bandwidth during LLM generation phase (even in the best-case scenario where they aren't physically bottlenecked on bandwidth to begin with, compared to the iGPU) since they generally have to read padded/dequantized data from main memory as they compute directly on that, as opposed to being able to unpack it in local registers like iGPUs can.
> usually on device prompts are very short
Sure, but that might change with better NPU support, making time-to-first-token quicker with larger prompts.
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The idea that tokenization is what they're for is absurd - you're talking a tenth of a thousandth of a millionth of a percent of efficiency gain in real world usage, if that, and only if someone bothers to implement it in software that actually gets used.
NPUs are racing stripes, nothing more. No killer features or utility, they probably just had stock and a good deal they could market and tap into the AI wave with.
Yes I said that in my comment. Yes they might be useful for that - but when you start getting to prompts that are long enough to have any significant compute time you are going to need far more RAM than these devices have.
Obviously in the future this might change. But as we stand now dedicated silicon for _just_ LLM prefill doesn't make a lot of sense imo.