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Comment by dangus | Hacker News Reader

Intel marketing isn’t the best but I am struggling to understand what issue you’re taking with this.

It’s an AI PC platform. It can do AI. It has an NPU and integrated GPU. That’s pretty straightforward. Competitors include Apple silicon and AMD Ryzen AI.

They’re predicting it’ll sell well, and they have a huge distribution network with a large number of partner products launching. Basically they’re saying every laptop and similar device manufacturer out there is going to stuff these chips in their systems. I think they just have some well-placed confidence in the laptop segment, because it’s supposed to combine the strong efficiency of the 200 series with the kind of strong performance that can keep up with or exceed competition from AMD’s current laptop product lineup.

Their naming sucks but nobody’s really a saint on that.

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i cant believe we're still putting NPUs into new designs.

silicon taken up that couldve been used for a few more compute units on the GPU, which is often faster at inference anyway and way more useful/flexible/programmable/documented.

Guess they're following Apple here whose NPUs get all the support possible, as far as I can tell.

Bingo. Maybe Microsoft shouldn’t even be chasing them but I think they have a point to try and stay competitive. They can’t just have their OS getting half the battery life of their main competitor.

When you use an Apple device, it’s performing ML tasks while barely using any battery life. That’s the whole point of the NPU. It’s not there to outperform the GPU.

You can thank Microsoft for that. Intel architects in fact did not want to waste area on an NPU. That caused Microsoft to launch their AI-whatever branded PCs with Qualcomm who were happy to throw in whatever Microsoft wanted to get to be the launch partner. After than Intel had to follow suit to make Microsoft happy.

That doesn’t explain why Apple “wastes” die area on their NPU.

The thing is, when you get an Apple product and you take a picture, those devices are performing ML tasks while sipping battery life.

Microsoft maybe shouldn’t be chasing Apple especially since they don’t actually have any marketshare in tablets or phones, but I see where they’re getting at: they are probably tired of their OS living on devices that get half the battery life of their main competition.

And here’s the thing, Qualcomm’s solution blows Intel out of the water. The only reason not to use it is because Microsoft can’t provide the level of architecture transition that Apple does. Apple can get 100% of their users to switch architecture in about 7 years whenever they want.

Every modern chip needs some percentage dedicated to dark silicon. There is no cheating the thermal reality. You could add more compute units in the GPU, but you then have to make up for it somewhere else. It’s a balancing act.

The Core Ultra lineup is supposed to be low-power, low-heat, right? If you want more compute power, pick something from a different product series.

> Every modern chip needs some percentage dedicated to dark silicon. There is no cheating the thermal reality. You could add more compute units in the GPU, but you then have to make up for it somewhere else. It’s a balancing act.

I think that "dark silicon" mentality is mostly lingering trauma from when the industry first hit a wall with the end of Dennard scaling. These days, it's quite clear that you can have a chip that's more or less fully utilized, certainly with no "dark" blocks that are as large as a NPU. You just need to have the ability to run the chip at lower clock speeds to stay within power and thermal constraints—something that was not well-developed in 2005's processors. For the kind of parallel compute that GPUs and NPUs tackle, adding more cores but running them at lower clock speeds and lower voltages usually does result in better efficiency in practice.

The real answer to the GPU vs NPU question isn't that the GPU couldn't grow, but that the NPU has a drastically different architecture making very different power vs performance tradeoffs that theoretically give it a niche of use cases where the NPU is a better choice than the GPU for some inference tasks.

NPUs aren't designed to be "faster", they are designed to have better perf/power ratios.

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