[vbezhenar]

For some people maybe. I don't want to use local AI and NPU will be dead weight for me. Can't imagine a single task in my workflow that would benefit from AI.

It's similar to performance/effiency cores. I don't need power efficiency and I'd actually buy CPU that doesn't make that distinction.

[wtallis]

> Can't imagine a single task in my workflow that would benefit from AI.

You don't do anything involving realtime image, video, or sound processing? You don't want ML-powered denoising and other enhancements for your webcam, live captions/transcription for video, OCR allowing you to select and copy text out of any image, object and face recognition for your photo library enabling semantic search? I can agree that local LLMs aren't for everybody—especially the kind of models you can fit on a consumer machine that isn't very high-end—but NPUs aren't really meant for LLMs, anyways, and there are still other kinds of ML tasks.

> It's similar to performance/effiency cores. I don't need power efficiency and I'd actually buy CPU that doesn't make that distinction.

Do you insist that your CPU cores must be completely homogeneous? AMD, Intel, Qualcomm and Apple are all making at least some processors where the smaller CPU cores aren't optimized for power efficiency so much as maximizing total multi-core throughput with the available die area. It's a pretty straightforward consequence of Amdahl's Law that only a few of your CPU cores need the absolute highest single-thread performance, and if you have the option of replacing the rest with a significantly larger number of smaller cores that individually have most of the performance of the larger cores, you'll come out ahead.

[Telaneo]

> You don't do anything involving realtime image, video, or sound processing?

Nothing that's not already hardware accelerated by the GPU or trivial to do on CPU.

> You don't want ML-powered denoising and other enhancements for your webcam

Not really.

> live captions/transcription for video

Not really, since they're always bad. Maybe if it's really good, but I haven't seen that yet.

> OCR allowing you to select and copy text out of any image

Yet to see this implemented well, but it would be a nice QOL feature, but not one I'd care all that much about being absent.

> object and face recognition for your photo library enabling semantic search?

Maybe for my old vacation photos, but that's a solid 'eh'. Nice to have, wouldn't care if it wasn't there.

[pmontra]

I provide a data point

* You don't do anything involving realtime image, video, or sound processing?

I don't

* You don't want ML-powered denoising and other enhancements for your webcam,

Maybe, but I don't care. The webcam is good or bad as it stands.

* live captions/transcription for video,

YouTube has them. I don't need it for live calls.

* OCR allowing you to select and copy text out of any image,

Maybe

* object and face recognition for your photo library enabling semantic search?

Maybe but I think that most people have their photo library on a cloud service that does AI in the cloud. My photos are on a SSD attached to a little single board ARM machine at home, so no AI.

What I would like to be able to do is running the latest Sonnet locally.

In general I think that every provider will do their best to centralize AI in their servers, much like Adobe did for their suite and Microsoft did for Office so local AI will be marginal, maybe OCR, maybe not even blurring the room behind my back (the server could do it.)

There are alternatives to Adobe and Office, because I don't care about more than the very basic functionality: running Gimp and Libreoffice on my laptop costs zero. How much would it cost to run Sonnet with the same performances of Claude's free tier? Start with a new machine and add every piece of hardware. I bet that's not a trivial amount of money.

[throwa356262]

Is everyone a content creator these days?

Besides, most of what you mentioned doesn't run on NPU anyway. They are usually standard GPU workload.

[wtallis]

None of what I listed was in any way specific to "content creators". They're not the only ones who participate in video calls or take photos.

And on the platforms that have a NPU with a usable programming model and good vendor support, the NPU absolutely does get used for those tasks. More fragmented platforms like Windows PCs are least likely to make good use of their NPUs, but it's still common to see laptop OEMs shipping the right software components to get some of those tasks running on the NPU. (And Microsoft does still seem to want to promote that; their AI PC branding efforts aren't pure marketing BS.)

[anematode]

The issue is that the consumer strongly associates "AI" with LLMs specifically. The fact that machine learning is used to blur your background in a video call, for example, is irrelevant to the consumer and isn't thought of as AI.

[fodkodrasz]

Never wanted to do high quality voice recognition? No need for face/object detection in near instant speed for your photos, embedding based indexing and RAG for your local documents with free text search where synonyms also work? All locally, real-time, with minimal energy use.

That is fine. Most ordinary users can benefit from these very basic use cases which can be accelerated.

Guess people also said this for video encoding acceleration, and now they use it on a daily basis for video conferencing, for example.

[alphager]

Those usecases are at least 5 years if not 10 years out. They require software support which won't come until a significant part of the pc market has the necessary hardware for it. Until then, paying extra for the hardware is foolish.

This will only come if Windows 12 requires a TPU and most of the old hardware is decommissioned.

[fisf]

Those use cases are already actively implemented, or being implemented in current software. The 5-10 year estimate is wildly wrong.

[orbital-decay]

Also similar to GPU + CPU on the same die, yet here we are. In a sense, AI is already in every x86 CPU for many years, and you already benefit from using it locally (branch prediction in modern processors is ML-based).

[wtallis]

> Also similar to GPU + CPU on the same die, yet here we are.

I think the overall trend is now moving somewhat away from having the CPU and GPU on one die. Intel's been splitting things up into several chiplets for most of their recent generations of processors, AMD's desktop processors have been putting the iGPU on a different die than the CPU cores for both of the generations that have an iGPU, their high-end mobile part does the same, even NVIDIA has done it that way.

Where we still see monolithic SoCs as a single die is mostly smaller, low-power parts used in devices that wouldn't have the power budget for a discrete GPU. But as this article shows, sometimes those mobile parts get packaged for a desktop socket to fill a hole in the product line without designing an entirely new piece of silicon.