Just saying “built on AI” or whatever isn’t a convincing sales pitch. What can I actually do with AI that will improve my day to day life? Not a single advert or pitch has told me a single use case for this that applies to what anyone would use for a personal computer, and they’re too risky to buy for employees in a work environment unless you can afford to be the guinea pig for this unproven line of hardware (in the sense that I know a ThinkPad will last 10 years but I have no idea how long a copilot pc will last, how often I need to replace the battery or ram or anything else). I’m aware of tech, I know what these laptops are, but as far as I can see the market for them just does not exist and I don’t understand why anyone would think otherwise.
What’s ironic is that the local llm/diffusion communities will not touch these. They’re just too slow, and impossibley finicky to set up with models big enough for people to actually want.
AMD’s next gen could change that, but they’ve already poisoned the branding. Good job.
It isn’t that surprising… they are shipping AI chips, but there is almost no software that makes use of them yet. Unless people require them for a certain use case, why bother?
Reminds me a bit of “5G ready” phone contracts, just meaningless marketing. Don’t need to chase every hype.
5g shot nobody asked for.
LTE is more secure anyway if that shit can be secure at all 🤡
I got one for a good price a few months ago to replace my dying Surface Pro 3.
No regrets, but I love it for the speed and the screen, not all the AI bullshit.
No issues no my end with ARM; it doesn’t play games well but I have a gaming PC for that.
What the hell do I need a laptop with an NPU for? I can write my own emails and documents. I don’t give a crap about the paint and notepad AI additions. I seriously cant think of any use case for copilot plus.
What is an NPU? Nonsense Processing Unit?
Neural, I think?
What kind of AI workloads are these NPUs good at? I mean it can’t be most of generative AI like LLMs, since that’s mainly limited by the memory bandwith and at this point it doesn’t really matter if you have a NPU, GPU or CPU… You first need lots of fast RAM and a wide interface to it.
That’s why NPU will have high bandwidth memory on chip. They’re also low precision to save power but massively parallel. A GPU and CPU can do it too, but less optimized.
That was my question… How much on-chip memory do they have? And what are applications for that amount of memory? I think an image generator needs like 4-5GB and a LLM that’s smart enough as a general porpose chatbot needs like 8-10GB. More will be better. And at that point you’d better make it unified memory like with the M-series Macs or other APUs? Or this isn’t targeted at generative AI but some other applications. Hence my question.
Last I heard this is for onboard speech recognition and basic image recognition/OCR so these things can more intelligently listen, see and store what you’re doing without sending it to a server. Not creepy at all.
I expect that a decent amount of sales of these are just people who don’t care replacing their PC.
I’d be shocked if there’s in any way a statistically significant amount of these purchases driven by the “ai features”.
Don’t forget corporations that lease new computers every few years.
And it’s hard to tell what the difference is. Apples ‘built from the ground up for AI’ chips just have more RAM. What’s the difference with CPUs? Do they just have more onboard graphics processing that can also be used for matrix multiplication?
The stupid difference is supposed to be that they have some tensor math accelerators like the ones that have been on GPUs for three generations now. Except they’re small and slow and can barely run anything locally, so if you care about “AI” you’re probably using a dedicated GPU instead of a “NPU”.
And because local AI features have been largely useless, so far there is no software that will, say, take advantage of NPU processing for stuff like image upscaling while using the GPU tensor calculations for in-game raytracing or whatever. You’re not even offloading any workload to the NPU when you’re using your GPU, regardless of what you’re using it for.
For Apple stuff where it’s all integrated it’s probably closer to what you describe, just using the integrated GPU acceleration. I think there are some specific optimizations for the kind of tensor math used in AI as opposed to graphics, but it’s mostly the same thing.
Seems silly to try to get the CPU to do GPU stuff, just upgrade the GPU.
The idea is having tensor acceleration built into SoCs for portable devices so they can run models locally on laptops, tablets and phones.
Because, you know, server-side ML model calculations are expensive, so offloading compute to the client makes them cheaper.
But this gen can’t really run anything useful locally so far, as far as I can tell. Most of the demos during the ramp-up to these were thoroughly underwhelming and nowhere near what you get from server-side services.
Of course they could have just called the “NPU” a new GPU feature and make it work closer to how this is run on dedicated GPUs, but I suppose somebody thought that branding this as a separate device was more marketable.
EU should introduce regulation that prohibits client-side AI/ML processing for applications that require internet access. Show the cost upfront. Let’s see how many people pay for that.
Basically yes. They come with an NPU (Neural processing unit) which is hardware acceleration for matrix multiplications. It cannot do graphics. Slap whatever NPU into the chip, boom: AI laptop!
Matrix multiplication is also largely what graphics cards do, I wonder how the npus are different.
Modern graphics cards pack a lot of functionality. Shading units, Ray tracing, video encoding/deciding. NPU is just the part needed to accelerat Neural nets.
But you can accelerate nural nets better with a GPU, right? They’ve got a lot more parallel matrix multiplication compute than any npu you can slap on a CPU.
It all depends on the GPU. If it’s something integrated in the CPU it will probably not so better, if it’s a 2000$ dedicated GPU with 48GB of VRAM is will be very powerful for Neural Net computing. NPUs are most often implemented as small, low-power, embedded solutions. Their goal is not to compete with data centers or workstations, it’s to enable some basic “AI” features on portable devices. E.g: “smart” camera with object recognition to give you alerts.
The Apple chips also have a wide interface to the RAM. That means you can run chatbots (LLMs) and other AI workloads that are memory-bound at crazy speeds compared to an Intel (or AMD) computer.
Really? How fast is the memory bus compared to x86? And did they just double the bus bandwidth by doubling the memory?
I’m dubious because they only now went to 16gb ram as base, which has been standard on x86 for almost a decade.
Apple is also much faster because the integrated graphics are actually usable for LLMs.
The base M is just a big faster than an Intel/AMD laptop if you can get their graphics working. The M Pro is 2x is fast (as its memory bus is 2x as wide). The M Max is 4x as fast.
AMD is coming out with something more competitive in 2025 though, Strix Halo.
”However, if it is performance you are concerned about, “it’s important to note that GPUs still far outperform NPUs in terms of raw performance,” Jessop said, while NPUs are more power-efficient and better suited for running perpetually.”
Ok, so if you want to run your local LLM on your desktop, use your GPU. If you’re doing that on a laptop in a cafe, get a laptop with an NPU. If you don’t care about either, you don’t need to think about these AI PCs.
Or use a laptop with a GPU? An npu seems to just be slightly upgraded onboard graphics.
It’s a power efficiency thing. According to the article, a GPU gets the job done, but uses more energy to get there. Probably not a big deal unless charging opportunities are scarce.
