AI PCs Aren't Good at AI: The CPU Beats the NPU

I think the results show that just in general the compute is not used well. That the CPU took 8.4ms and GPU took 3.2ms shows a very small gap. I'd expect more like 10x - 20x difference here. I'd assume that the onnxruntime might be the issue. I think some hardware vendors just release the compute units without shipping proper support yet. Let's see how fast that will change.

Also, people often mistake the reason for an NPU is "speed". That's not correct. The whole point of the NPU is rather to focus on low power consumption. To focus on speed you'd need to get rid of the memory bottleneck. Then you end up designing your own ASIC with it's own memory. The NPUs we see in most devices are part of the SoC around the CPU to offload AI computations. It would be interesting to run this benchmark in a infinite loop for the three devices (CPU, NPU, GPU) and measure power consumption. I'd expect the NPU to be lowest and also best in terms of "ops/watt"

> Also, people often mistake the reason for an NPU is "speed". That's not correct. The whole point of the NPU is rather to focus on low power consumption.

I have a sneaking suspicion that the real real reason for an NPU is marketing. "Oh look, NVDA is worth $3.3T - let's make sure we stick some AI stuff in our products too."

I assume you're both right. I'm sure NPUs exist to fill a very real niche, but I'm also sure they're being shoehorned in everywhere regardless of product fit because "AI big right now."

The shoehorning only works if there is buyer demand.

As a company, if customers are willing to pay a premium for a NPU, or if they are unwilling to buy a product without one, it is not your place to say “hey we don’t really believe in the AI hype so we’re going to sell products people don’t want to prove a point”

Apple will have a completely AI capable product line in 18 months, with the major platforms basically done.

Microsoft is built around the broken Intel tick/tick model of incremental improvement — they are stuck with OEM shitware that will take years to flush out of the channel. That means for AI, they are stuck with cloud based OpenAI, where NVIDIA has them by the balls and the hyperscalers are all fighting for GPU.

Apple will deliver local AI features as software (the hardware is “free”) at a much higher margin - while Office 365 AI is like $400+ a year per user.

You’ll have people getting iPhones to get AI assisted emails or whatever Apple does that is useful.

We're still looking for "that is useful".

The stuff they've been trying to sell AI to the public with is increasingly looking as absurd as every 1978 "you'll store your recipes on the home computer" argument.

AI text became a Human Centipede story: Start with a coherent 10-word sentence, let AI balloon it into five pages of flowery nonsense, send it to someone else, who has their AI smash it back down to 10 meaningful words.

Coding assistance, even as spicy autocorrect, is often a net negative as you have to plow through hallucinations and weird guesses as to what you want but lack the tools to explain to it.

Image generation is already heading rapidly into cringe territory, in part due to some very public social media operations. I can imagine your kids' kids in 2040 finding out they generated AI images in the 2020s and looking at them with the same embarrassment you'd see if they dug out your high-school emo fursona.

There might well be some more "closed-loop" AI applications that make sense. But are they going to be running on every desktop in the world? Or are they going to be mostly used in datacentres and purpose-built embedded devices?

I also wonder how well some of the models and techniques scale down. I know Microsoft pushed a minimum spec to promote a machine as Copilot-ready, but that seems like it's going to be "Vista Basic Ready" redux as people try to run tools designed for datacentres full of Quadro cards, or at least high-end GPUs, on their $299 HP laptop.