Nvidia has massive advantage in the number of compute clusters thanks to their separate chip design + more compact hierarchy. Apple has better hardware utilization. I think if Apple wants to compete they will need to invest in more expensive packaging technologies (which wouldn’t be cost effective for Nvidia) as well as make their clusters denser. I think there is some evidence that they are building the fundament for future iterations.
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M3 Max GPU vs NVIDIA RTX 4090 Laptop
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Nvidia has massive advantage in the number of compute clusters thanks to their separate chip design + more compact hierarchy. Apple has better hardware utilization. I think if Apple wants to compete they will need to invest in more expensive packaging technologies (which wouldn’t be cost effective for Nvidia) as well as make their clusters denser. I think there is some evidence that they are building the fundament for future iterations.
Anything that uses floating point operations takes advantage of it. Blender supports both Optix and CUDA backends. Here you can inspect the scores by backend type:
Blender - Open Data
Blender Open Data is a platform to collect, display and query the results of hardware and software performance tests - provided by the public.
What it is interesting (to me anyway) is how little increase some of the Nvidia cards get from Optix. The 4090 only gets 50%. I thought it was much more.Anything that uses floating point operations takes advantage of it. Blender supports both Optix and CUDA backends. Here you can inspect the scores by backend type:
Blender - Open Data
Blender Open Data is a platform to collect, display and query the results of hardware and software performance tests - provided by the public.opendata.blender.org
The 4080 laptop version is thereabout with the desktop 3090 at games too (https://www.notebookcheck.net/Nvidi...erformance-and-power-efficiency.691313.0.html),
Ada generation is a big improvement on Ampere at anything above 4070 level.
To claim "M2 Ultra is faster than 3080 and a hair away from 4070", @bcortens use the scores of M2 Ultra using Metal and 3080 using CUDA. So, I understood that @komuh was asking if comparing M2 Ultra using Metal and 3080 using CUDA was fair or not.
Given Nvidia's focus on Blender's OptiX backend since 2019, I doubt Blender's CUDA backend will take good advantage of Nvidia's latest GPUs.
Ah, I see. Thanks for explaining the background better.
I’d say that the CUDA backend is mature enough to be used for comparisons like these. I would be surprised if it needed patches to take better advantage of the new GPUs. Note that Metal and CUDA use pretty much the same codebase too.
I chose CUDA as my intention was to show raw compute capability comparisons, rather than have that masked by the fact that Apple didn’t yet have RT cores and NVIDIA did.
Any Idea of dis/advantage of these M3/128GB/4TB vs NVIDIA 4090 Laptop thaaat goes max 36GB eventually 64 GB RAM when it comes to kohya GUI / Dreambooth image based machine learning ???
Do you mean larger limit or only speed/ what about model training with Dreambooth? If you do upgrade on 128 GB RAM
A 16" M3 MBP with a top-of-the-line Max and 64Gb will cost pretty close to a pair of 24Gb desktop 4090s. In most cases, though it would take some extra effort, the kind of stuff that GPUs do will run just fine split between two cards, so it would probably be significantly faster, most of the time. Cards often use inboard RAM that is faster than main board RAM.
The open question is, what if you really do need that extra memory? A 48Gb MBP would give the GPU 35~40Gb of RAM to play with, which is quite a lot more than the card. If you cannot afford two cards, or your work fails to divide nicely, I would imagine that the slower UMA would be more efficient (and ultimately faster) than GDDRx embroiled in a starvation swapfest.
The open question is, what if you really do need that extra memory? A 48Gb MBP would give the GPU 35~40Gb of RAM to play with, which is quite a lot more than the card. If you cannot afford two cards, or your work fails to divide nicely, I would imagine that the slower UMA would be more efficient (and ultimately faster) than GDDRx embroiled in a starvation swapfest.
Ok… my main fear was disadvantage of lack of access to computing platform CUDA .. 128 RAM is insane advantage for AI model training in a longer term…stable diffusion… upscale etc. and for unified memory with ambition to take a part in AI machine learning ( dreambooth etc.) it is playing quite a role …40 core GPU deciding… beside i do simultan run of couple of platforms as Rhinoceros/ UnrealE or Cinema and Photoshop … and I am usually not changing Laptop that often..
Yeah, the one problem is that OpenCL is actually open. Cuda is great, but it's not open...
If people would use open standards code could also be system agnostic....
In hacking, I believe in system agnostics and that things should run on as many platforms as possible.
That's fine, but the reality is that OpenCL lost badly to CUDA, so it's not very relevant. Even Apple thinks so - despite being the company which invented OpenCL and donated it to Khronos to use as an open standard, they've deprecated OpenCL in macOS. "Deprecated" status is how Apple communicates that they plan to remove an API at some point in the future.
Much like OpenGL, OpenCL seems to be sticking around for years past the date Apple tagged it as deprecated, but you can't count on it being there forever, and they aren't updating these to keep up with new features released by Khronos.
It all depends on the software.
I spend some hours every day using 3D CAD. Autodesk Fusion360 now runs native on Apple Silicon but they don't directly use Apple's APIs. They are doing their rendering using single-threaded OpenGL. I just did a ray-trace render of a new 3D printer I'm working on so I could get some comments. It took 6 minutes for one frame.
So we are not taking frames per second, but minutes per frame on an M2-Pro. I looked at Apple's "Activity Monitor" app and noticed that Fusion360 is using one of the 16 GPU cores and all ten CPU cores.
I don't mind much because I only do a few renders a week and I can do other things while I wait.
Only benchmarks take full advantage of the hardware. Most software doesn't. Fusion360 is an extreme example.
So far, there aren't many software to compare and even then, they are all fundermantally optimized with CUDA and Nvidia software as Nvidia dominates the GPU market. AMD sucks and not even close to Nvidia to compete especially with software.
With GPU benchmarks, RTX 40 series are far better but at this point, it's hard to prove it since Mac lacks a lot of software to test with compared to PC especially native versions as almost all software are optimized for Nvidia.
Apple has no power to bring many software to Mac which is a huge problem. Beside, AS Mac's limitation is too low due to lack of Mac Pro with 4x high-end GPU. Mac's software are too focused on 2D based software such as video, audio, photo, illustrate, and more. 3D, games, AI, research, and others aren't great on Mac.
So they probably don't use GPU for anything other then displaying stuff.
Not on handbrake H265 encode, I am amazed that using the Apple Videotoolbox encoder beats NVEC on the Lenovo Legion Pro 7i 13900HX and 4090.
Is the compression level and quality equivalent? I'm still using software encoding on my M3Max as I'm still disappointed in video toolbox compression level/quality ratio. Software encoding, while slower, is still much better quality wise and final file size.
Yes, I had to find the proper way to select the video toolbox. It’s in a different place than just pulling down the menu for the video encoder. If I just pull down the video encoder menu, the quality is lower, but if you also select it in the preset => hardware list, it is quite high.
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Nvidias encode quality is sadly far superior to Apple's, even at lower bitrates than Apple.
Interesting, I found similar results to the people commenting here:
Quality of video encoding (VideoTo… | Apple Developer Forums
I've had a lot of strange artefacts exporting on my M1 machine that my Nvidia machine just doesn't show. I still use it for quick exports etc. but find the quality definitely lacking (and I've tried multiple settings!). Oh and agreed, Intels implementation is excellent.
Thanks. I wonder if the new M3 max has fixed some of these things? Part of me thinks this is a setting issue.Interesting, I found similar results to the people commenting here:
Quality of video encoding (VideoTo… | Apple Developer Forums
forums.developer.apple.com
I've had a lot of strange artefacts exporting on my M1 machine that my Nvidia machine just doesn't show. I still use it for quick exports etc. but find the quality definitely lacking (and I've tried multiple settings!). Oh and agreed, Intels implementation is excellent.
