AMD marketing is hoping the “AI” branding is a positive. Antidotally, I know many consumers who are not sold on AI. This branding could actually hurt sales.
But does this extrapolate to the current way of doing AI being in normal life in a good way that ends up being popular? The way Microsoft etc is trying to put AI in everything is kinda saying no it isn’t actually what users want.
I’d like voice control in my PC or phone. That’s a use for these NPUs. But I imagine it is like AR- what we all want until it arrives and it’s meh.
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.
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.
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.
This will only come if Windows 12 requires a TPU and most of the old hardware is decommissioned.
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.
Besides, most of what you mentioned doesn't run on NPU anyway. They are usually standard GPU workload.
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.)
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.
* 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.
Your true believer convictions don't matter here. Those AI accelerators are merely just marketing stunts. They won't help your local inference because they are not general purpose enough for that, they are too weak to be impactful, most people won't ever run local inference because it sucks and is a resource hog most can't afford, and it goes against the interests of those scammy unprofitable corporations who are selling us LLMs as AI as the silver bullet to every problem and got us there in the first place (they are already successful in that, by making computing unaffordable). There's little to no economical and functional meaning to those NPUs.
a) Local inference for chats sucks. Using LLMs for chatting is stupid though.
b) Local inference is cheap if you're not selling a general-purpose chatbot.
There's lots of fun stuff you can get with a local LLM that previously wasn't economically possible.
Two big ones are gaming (for example, text adventure games or complex board games like Magic the Gathering) and office automation (word processors, excel tables).
/r/SillyTavernAI would disagree with you.
We are talking about NPUs here.
> If you intend to do LLM inference on your local machine, we recommend a 3000-series NVIDIA graphics card with at least 6GB of VRAM, but actual requirements may vary depending on the model and backend you choose to use.
Also, please be respectful when discussing technical matters.
P.S. I didn't say "local chat sucks".
...which is not by any means a powerful GPU, and besides the AMD Ryzen AI CPUs in question have a plenty enough capacity to run local LLMs esp. MoE ones; with 3b active MoE parameters miniPC equipped with these CPUs dramatically outperform any "3000-series NVIDIA graphics card with at least 6GB of VRAM".
> please be respectful when discussing technical matters.
That is more applicable to your inappropriately righteous attitude than to mine.
Yes, gaming is (of course) a big use case for LLMs.
Ask your friends or a small business owner if they are going to spend $1k on a new laptop because "there's lots of fun stuff".
For office automation, you'll get a lot more mileage with Claude and similar.
Do people not buy gaming PCs and game consoles? Isn't that buying something because "there's lots of fun stuff?"
And while sure a business owner wouldn't be buying it for "fun stuff", if it was about being able to run the AI tools they want without the business risk of sending your most important data and intellectual property to an AI provider wouldn't some think about it?
If you can use the NPU to process embeddings quickly, you get some incredible functionality — from photo search by subject to near match email search.
For consumer applications that’s what I’m most excited for. It takes something that used to require large teams, data, and bespoke models into commodity that any app can use.
Do you have a real argument, especially a technical one, that you can contribute?
In what way precisely? That local LLMs "suck"? Is that a technical argument? Or this statement "there's little to no economical and functional meaning to those NPUs." - is that actual factual statement or a emotionally charged verbal flatulence? and what "they won't help your local inference because they are not general purpose enough for that" even means? People succesfully run largeish MoE llms on AMD Ryzen AI miniPCs.
> Do you have a real argument, especially a technical one, that you can contribute?
What kind of argument do you want me to "contribute" wrt the ideological rant the "parent comment" had managed to produce?
- I shouldn't be paying more for my next CPU because it has a NPU that I won't ever use. Give me the freedom of choice.
- Given that freedom of choice, it would seem that a majority would opt-out (as seen recently by Dell), so the morals of all that are dubious.
- NPUs may not be completely stupid as a concept, in theory, but at this point in time they are proprietary black-boxes purpose-built for marketing and micro-benchmarks. Give me something more general-purpose and open, and I will change my mind
- …but the problem is, you can only build so much general-purpose computing in bespoke processor. That's kind of its defining trait. So I won't hold my breath.
- Re: local-inference for the masses, putting aside the NPU shortcomings from above: how large do you think a LLM needs to be so it's deemed useful by your average laptop user? How would the inference story be like, in your honest opinion (in terms of downloading the model, loading it in memory, roundtrip times)? And how often would the user realistically want to suffer through all that, versus, just hopping to ${favorlite_llm.ai} from their browser?
Anyhow, if that makes me "antiai", please, sign me up!
There is a plenty to choose from.
> - NPUs may not be completely stupid as a concept, in theory, but at this point in time they are proprietary black-boxes purpose-built for marketing and micro-benchmarks. Give me something more general-purpose and open, and I will change my mind
In fact the linked article is not talking about NPUs in particular, but about Ryzen AI cpus. These have unified memory and more compute compared to normal ones which make them very useful for inference.
> how large do you think a LLM needs to be so it's deemed useful by your average laptop user?
Depend what they need it for. Useful autcomplete in IDE starts at around 4b weights.
> loading it in memory
Happens only once, usually takes around 10sec.
> roundtrip times
Negligible? it is loca after all.
> And how often would the user realistically want to suffer
No suffering involved.
You have fallen headfirst into the "Not now, so never" fallacy. As if consumer hardware won't get more powerful, or models more economical.
Perhaps. Though we have empirical evidence of how much we can quantize and distillate models to the point of practical uselessness. That sets a bar for how large a local model needs to be for general-use as to compete with the could ones. We are talking in the area of 60GB for GPT-OSS/Qwen3.5, which is what enthusiasts are running on 32GB DDR5 + 24GB VRAM RTX 3090.
> As if consumer hardware won't get more powerful
Now I will let you, with that last fact in hand, plot a chart of how much it's been costing to provision that over the past 2 years and use it to prove me wrong about the affordability of local models.
Toilets also changed everything we do and are helpful in unobtrusive ways, but that won't make the "Ryzen Crapper" a customer favorite.
No one is running LLMs on current gen NPUs so if we will in the future its a long time coming. Unless they can demonstrate some real (and not marketing) wins I remain skeptical that a large NPU for LLMs is the future.
I can totally see NPU accelerating simple tasks, but to be worth the silicon they have a ways to go imo.
99% of people don't need or want a dev workstation. My travel laptop is 7+ years old and I couldn't tell you the difference between it and a current flagship in terms of browsing and everyday tasks.
I will not lie, I find LLMs useful but the desktop experience is pretty polished already. NPUs seem to be an attempt to ride the AI bandwagon with very little to show for it so far.