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this post was submitted on 15 Nov 2024
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What kind of AI workloads are these NPUs good at? I mean it can't be most of generative AI like LLMs, since that's mainly limited by the memory bandwith and at this point it doesn't really matter if you have a NPU, GPU or CPU... You first need lots of fast RAM and a wide interface to it.
That's why NPU will have high bandwidth memory on chip. They're also low precision to save power but massively parallel. A GPU and CPU can do it too, but less optimized.
That was my question... How much on-chip memory do they have? And what are applications for that amount of memory? I think an image generator needs like 4-5GB and a LLM that's smart enough as a general porpose chatbot needs like 8-10GB. More will be better. And at that point you'd better make it unified memory like with the M-series Macs or other APUs? Or this isn't targeted at generative AI but some other applications. Hence my question.
Last I heard this is for onboard speech recognition and basic image recognition/OCR so these things can more intelligently listen, see and store what you're doing without sending it to a server. Not creepy at all.