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Comment by intrasight | Hacker News Reader

Honest question: Why would I (or most users) care? If I have a Mac, I'm going to get the performance of that machine. If I have a gaming PC, I'll get the performance of that machine. If I have both, I'm still likely to use whichever AI is running on my daily driver.

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I have both. I drive both depending on context of my day and where I want to work. If I were interested in this, and the 4090 were faster, I'd just ssh into that machine from my Mac if I wanted to use my Mac -- and presumably, vice versa.

Curiosity for example.

Or having a better image of performance in your head when you buy new hardware.

It's just a blog article. The time and effort to make it and to consume it is not in the range of millions there is no need for 'more'.

Suppose you’re a serious hobbyist, wanting to buy the most powerful consumer device for deep learning. Then the choice of buying a RTX 4090 or a M3 Mac is probably quite interesting.

Personally, I have already a RTX 3090, so for me it’s interesting if a M3 would be a noticeable upgrade (considering RAM, speed, library support)

It's really not interesting. The Nvidia card is far superior.

The super optimized code for a network like this is one test case. General networks are another.

> The Nvidia card is far superior.

I mean, you're probably right, but MLX framework was released just a week ago so maybe we don't really know what it's capable of yet.

The "available VRAM" is the big difference. You get a lot more to use on an apple silicon machine for much cheaper than you get on an nvidia card on a $/GB basis, especially if you want to pass the 24GB threshold. You can't use NVIDIA consumer cards after that and there is a shortage of them.

The power:perf ratios seem about equal although, so it's really up to apple to release equivalently sized silicon on their desktop to really have this be a 1:1 comparison.

I wonder how complicated it would be to make happen / how likely it is to happen, for the Apple way of sharing memory between CPU and GPU to become a thing on PCs. Could NVIDIA do it unilaterally or would it need to be in partnership with other companies?

Could anyone who understands this hardware better than me chime in on the complexities of bringing unified ram/vram to PC, or reasons it hasn't happened yet?

AMD built "Heterogeneous System Architecture" for their APUs to do the same thing, but since their APUs all ended up mostly targeting cheap/low-power/low-profile laptops and business machines, it was never actually utilized (from what I've been able to find, there's no public API to actually use this as designed and you still have to copy things from "system RAM" to "GPU RAM" even though the hardware was designed to support zero-copy, whereas Apple has made it all automatic when you use Metal)

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