Honestly, you can train basic models just fine on M-Series Max MacBook Pros.
Do you also compare cars by looking at only the super expensive limited editions, with every single option box ticked?
I'd also point out that said 3 year old $1999 Mac Studio that I'm typing this on already runs ML models usefully, maybe 40-50% of the old 3000-series Nvidia machine it replaces, while using literally less than 10% of the power and making a tiny tiny fraction of the noise.
Oh, and it was cheaper. And not running Windows.
For training the Macs do have some interesting advantages due to the unified memory. The GPU cores have access to all of system RAM (and also the system RAM is ridiculously fast - 400GB/sec when DDR4 is barely 30GB/sec, which has a lot of little fringe benefits of it's own, part of why the Studio feels like an even more powerful machine than it actually is. It's just super snappy and responsive, even under heavy load.)
The largest consumer NVidia card has 22GB of useable RAM.
The $1999 Mac has 32GB, and for $400 more you get 64GB.
$3200 gets you 96GB, and more GPU cores. You can hit the system max of 192GB for $5500 on an Ultra, albeit it with the lessor GPU.
Even the recently announced 6000-series AI-oriented NVidia cards max out at 48GB.
My understanding is a that a lot of enthusiasts are using Macs for training because for certain things having more RAM is just enabling.
I keep hearing this meme of Mac's being a big deal in LLM training, but I have seen zero evidence of it, and I am deeply immersed in the world of LLM training, including training from scratch.
Stop trying to meme apple M chips as AI accelerators. I'll believe it when unsloth starts to support a single non-nvidia chip.
And while the Mac Studio has a lot of memory bandwidth compared to most desktops CPUs, it isn't comparable to consumer GPUs (the 3090 has a bandwidth of ~936GBps) let alone those with HBM.
I really don't hear about anyone training on anything besides NVIDIA GPUs. There are too many useful features like mixed-precision training, and don't even get me started on software issues.
But for individual developers it’s an interesting proposition.
And a bigger question is: what if you already have (or were going to buy) a Mac? You prefer them or maybe are developing for Apple platforms.
Upping the chip or memory could easily be cheaper than getting a PC rig that’s faster for training. That may be worth it to you.
Not everyone is starting from zero or wants the fastest possible performance money can buy ignoring all other factors.
It's just more efficient to offload training to cloud Nvidia GPUs
I simply want to point out that these folks don't really care about that. They want a Mac for more reasons than "performance per watt/dollar" and if it's "good enough", they'll pay that Apple tax.
Yes, yes, I know, it's frustrating and they could get better Linux + GPU goodness with an nVidia PC running Ubuntu/Arch/Debian, but macOS is painless for the average science AI/ML training person to set up and work with. There are also known enterprise OS management solutions that business folks will happily sign off on.
Also, $7000 is chump change in the land of "can I get this AI/ML dev to just get to work on my GPT model I'm using to convince some VC's to give me $25-500 million?"
tldr; they're gonna buy a Mac cause it's a Mac and they want a Mac and their business uses Mac's. No amount of "but my nVidia GPU = better" is ever going to convince them otherwise as long as there is a "sort of" reasonable price point inside Apple's ecosystem.
You can't lug around a desktop workstation.
I am honestly shocked Nvidia has been allowed to maintain their moat with cuda. It seems like AMD would have a ton to gain just spending a couple million a year to implement all the relevant ML libraries with a non-cuda back-end.
Intel seems like they could have some interesting stuff in the annoyingly named “OneAPI” suite but I ran it on my iGPU so I have no idea if it is actually good. It was easy to use, though!