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..).
