Nothing compared to Nvidia though. The FLOPS and memory bandwidth is simply not there.
Besides size, heat, fan noise, and not having to build it yourself, this is the only area where Apple Silicon might have advantage over a homemade 4090 rig.
Sure, whisper.cpp is not an LLM. The 4090 can't even do inference at all on anything over 24GB, while ASi can chug through it even if slightly slower.
I wonder if with https://github.com/tinygrad/open-gpu-kernel-modules (the 4090 P2P patches) it might become a lot faster to split a too-large model across multiple 4090s and still outperform ASi (at least until someone at Apple does an MLX LLM).
Common LLM runners can split model layers between VRAM and system RAM; a PC rig with a 4090 can do inference on models larger than 24G.
Where the crossover point where having the whole thing on Apple Silicon unified memory vs. doing split layers on a PC with a 4090 and system RAM is, I don't know, but its definitely not “more than 24G and a 4090 doesn't do anything”.
Sure and ASi can do inference on models larger than the Unified Memory if you account for streaming the weights from the SSD on-demand. That doesn't mean it's going to be as fast as keeping the whole thing in RAM, although ASi SSDs are probably not particularly bad as far as SSDs go.
There are benchmark data showing that an Apple M2 Ultra is 47% and 60% slower against Xeon W9 and RTX 4090, or 0.35% and 2% slower against i9-13900K and RTX 4060 Ti, respectively, in Geekbench 5 Multi-threaded and OpenCL Compute tests.
Apple Silicon Macs are NOT faster than competing desktop computers, nor M1 was massively faster than NVIDIA 3070(Desktop - 2x faster than Laptop variant M1 was compared against) for that matter. They just offer up to 128GB shared RAM/VRAM options in slim desktops and laptops, which is handy for LLM, that's it.
Please stop taking Apple marketing materials at full face value or above. Thank you.
1: https://i.extremetech.com/imagery/content-types/03ekiQwNudC75iOK4AMuEkw/images-2.jpg
2: screenshot from[4]: https://www.igorslab.de/wp-content/uploads/2023/06/Apple-M2-ULtra-SoC-Geekbench-5-Multi-Threaded.jpg
3: screenshot from[4]: https://www.igorslab.de/wp-content/uploads/2023/06/Apple-M2-ULtra-SoC-Geekbench-5-OpenCL-Compute.jpg
4: https://wccftech.com/apple-m2-ultra-soc-isnt-faster-than-amd-intel-last-year-desktop-cpus-50-slower-than-nvidia-rtx-4080/Even the A100 for something around $15,000 (edit: used to say $10,000) only goes up to 80 gigabytes of VRAM, but a 192GB Mac Studio goes for under $6,000.
Those figures alone proves Nvidia isn't even competing in the consumer or even the enthusiast space anymore. They know you'll buy their hardware if you really need it, so they aggressively segment the market with VRAM restrictions.
A100 80GB goes for around $14,000 - $20,000 on eBay and A100 40GB goes for around $4,000 - $6,000. New (not from eBay - from PNY and such), it looks like an 80GB would set you back $18,000 to $26,000 depending on whether you want HBM2 or HBM2e.
Meanwhile you can buy a Mac Studio today without going through a distributor and they're under $6,000 if the only thing you care about is having 192GB of Unified Memory.
And while the memory bandwidth isn't quite as high as the 4090, the M-series chips can run certain models faster anyway, if Apple is to be believed