Actually that's a really good question, I hadn't considered that the comparison here is just CPU vs using Metal (CPU+GPU).
To answer the question though - I think this would be used for cases where you are building an app that wants to utilize a small AI model while at the same time having the GPU free to do graphics related things, which I'm guessing is why Apple stuck these into their hardware in the first place.
Not switching to the CPU - switching to the ANE (Neural Cores) - if you read the research papers Apple has released - the example I gave is pretty much how it's being used - small image classification models running on the ANE, alongside a graphics app that needs the GPU to be free.
your tests are wrong. you used MLX for Mac Studio (optimized for Apple Silicon) but you didn't use vLLM for 5090. There's no way a machine with half the bandwidth of 5090 delivers twice as fast tok/s.
They're basically second place behind NVIDIA for model inference performance and often the only game in town for the average person if you're trying to run larger models that wont fit in the 16 or 24gb of memory available in top-shelf NVIDIA offerings.
I wouldn't say Apple isn't serious about AI, they had the forethought to build the shared memory architecture with the insane memory bandwidth needed for these types of tasks, while at the same time designing neural cores specifically for small on-device models needed for future apps.
I'd say Apple is currently ahead of NVIDIA in just sheer memory available - which for doing training and inference on large models, it's kinda crucial, at least right now. NVIDIA seems to be purposefully limiting the memory available in their consumers cards which is pretty short sighted I think.
Not true. It performs 20-30% better than a RTX A6000 (I have both). Except it has more than 10 times the VRAM.
For a comparison with newer Nvidia cards, benchmarks say it does substantially better than a 5070ti, a bit better than a 4080, and a bit worse than a 5080.
But once again, it got 30 times the vram amount of the mentioned cards, which for AI workloads are just expensive toys due to lack of vram indeed.
It can run models that cannot fit on TEN rtx 5090s (yes, it can run DeepSeek V3/R1, quantized at 4 bit, at a honest 18-19 tok/s, and that's a model you cannot fit into 10 5090s..).
Can we stop with the derisive “fanboy” nonsense? Most people don’t say “FOSS” fanboy or Linux “fanboy” — but plenty of people here are exactly that. It’s a bit insulting to people that like and appreciate Mac hardware; just because you might not like it doesn’t mean you have to be so dismissive. And that Mac Studio is a very impressive computer — but it’s usually the ones that have never used on that seem to have to most opinions about them.
