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Comment by aleph_minus_one | Hacker News Reader

> NPUs aren't meant for LLMs. There are a lot more neural net tech out there than LLMs.

OK, but where can I find demo applications of these that will blow my mind (and make me want to buy a PC with an NPU)?

Apple demonstrates this far better. I use their Photos app to manage my family pictures. I can search my images by visible text, by facial recognition, or by description (vector search). It automatically composes "memories" which are little thematic video slideshows. The FaceTime camera automatically keeps my head in frame, and does software panning and zooming as necessary. Automatic caption generation.

This is normal, standard, expected behavior, not blow your midn stuff. Everyone is used to having it. But where do you think the computation is happening? There's a reason that a few years back Apple pushed to deprecate older systems that didn't have the NPU.

I've yet to see any convincing benchmarks showing that NPUs are more efficient than normal GPUs (that don't ignore the possibility of downclocking the GPU to make it run slower but more efficient)

NPUs are more energy efficient. There is no doubt that a systolic array uses less watts per computation than a tensor operation on a GPU, for these kinds of natural fit applications.

Are they more performant? Hell no. But if you're going to do the calculation, and if you don't care about latency or throughput (e.g. batched processing of vector encodings), why not use the NPU?

Especially on mobile/edge consumer devices -- laptops or phones.

> NPUs are more energy efficient. There is no doubt

Maybe because they sleep all the time. To be able to use an NPU you need at least a compiler which generates code for this particular NPU and a CPU scheduler which can dispatch instructions to this NPU.

https://fastflowlm.com/benchmarks/

https://fastflowlm.com/assets/bench/gemma3-4b.png

Best NPU app so far is Trex for Mac.

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