Doesn't look too bad for Nvidia, especially when you consider 3090 is still on Samsung 8nm, which is equivalent to TSMC 10nm, compared to 5nm on Apple M1.

Regardless, Apple's threat to vendors like that is their complete vertical integration. Ran some of the new object capture code (photogrammetry) on my M1 Mac yesterday and in no time at all the 11 trillion op neural engine blasted through and generated a remarkable model. We've seen Apple respond to discovered performance needs by plugging in a matrix engine, a neural engine, and scaling appropriately, dedicating cores and silicon to the greatest needs. They are in a very unique position relative to someone like nvidia who effectively throws something over a fence.

Also, Apple desperately needs to support a real graphics API. Metal is a joke, and even the translation tools like MoltenVK, while impressive, still end up beholden to Apple's arbitrary limitations. If they don't end up supporting Vulkan on the M1, it's a moot point for me. You could have the most powerful GPU in the world, but I won't use it if it's bottlenecked by the shittiest modern graphics API.

The GPU in the AS M1 is the fastest integrated graphics available in the mainstream computing market [1]. That is the competition, not a standalone, 120W GPU. Apple is purportedly now working on separating their GPU designs into a much larger heat and power profile (which contrary to some of the comments on here clearly isn't going to be for laptops, beyond an external TB4 enclosure) and it might just change things a bit.

Scaling a GPU is easier than scaling a CPU, by design. Apple's GPU has nothing to do with ARM.

And to your original point, yes, Apple does largely choose which workloads run on Apple Silicon and how. By controlling the APIs along with the silicon, Apple abstracts it to a degree that gives them enormous flexibility. The Accelerate and CoreML APIs are abstract vehicles that might use one or a thousand matrix engines, neural nets, or an array of GPUs. Apple has built a world where they have more hardware flexibility than anyone. And while close to no one is doing model training on Apple hardware right now, Apple has laid the foundation so a competitive piece of hardware could change that overnight.

[1] The SoC graphics of the chips in the PS5 and Xbox Series X have more powerful graphics, but the GPU chiplet alone on those systems uses a magnitude more power and more die than the entire M1 SoC. In another comment you mentioned that Zen 2 integrated graphics come close. They aren't within a ballfield, literally with 1/4 or worse the performance. In discussions like this unfortunately the boring "n years old / n process" trope is used to excess, yet again there are zero competitive integrated graphics on the market. None. Apple isn't a GPU company, yet here we are.

Bzzzzt

We aren't comparing performance-per-watt, we're comparing raw compute for the price.

All while Apple keeps executing superbly. Eh.

It would be great if they did it the eGPU route with TB4, in a slick package

We’ve only see the Apple equivalent of the i3 with integrated graphics. It’s going to get interesting over the next several months as Apple unveils their middle and upper performance solutions.

To get the M1 warm, I need to be charging it while also playing an Intel based game like the latest Subnautica, with the machine on my lap. Even then, it's not as hot as my Macbook Pro gets while unplugged and browsing the internet.

That is exactly what the rumours suggest Apple is working on.

I was talking about the mobile GPU on MacBook Pros which is based on a 14nm chip. The full name is Radeon Pro Vega 20:

https://www.amd.com/en/graphics/radeon-pro-vega-20-pro-vega-...

https://www.techpowerup.com/gpu-specs/radeon-pro-vega-20.c32...

Vega 20 seems to also refer to a discrete GPU. This has been later rebranded to Radeon VII (maybe because of this confusion). The number you are quoting is for the discrete GPU.

It's extremely doubtful any new Apple Silicon device will come out with AMD GPUs.

Would like to see some benchmarks with that sort of hardware or with a 6900XT, driver support which came to MacOS only recently. Now I just have an M1 Mac Mini & a PC with Nvidia.

In context of ML, nvidia’s flagship is the A100, which has 312 TFLOPS. You can also compare with a TPU device which has 180 TFLOPS (v2) or 420 TFLOPS (v3). You can use at least the TPU v2 reliably on colab for free.

FP16 performance is also relevant as a lot of people now train in FP16. The default for pytorch/TF is still FP32.