> Is there somebody not researching faster llm performance?
The surprising bit is less about what they're working on and more about the collaboration itself, including the mutual and coordinated co-marketing/PR. Apple and Nvidia haven't had a business relationship in over a decade, ever since Apple stopped using Nvidia GPUs in Macs.
This sort of re-engagement and subsequent promotion isn't something that "just happens", in my experience. It's reasonably possible that this could portend additional future outcomes, such as official support for Nvidia-based eGPUs, Apple's licensing of/support for CUDA, Mac Pros with Nvidia GPUs, etc.
That is exactly what I was going to say. Nvidia has been a big No No inside Apple after they strained there relationship with MacBook Pro recall with Nvidia GPU causing overheating. Apple's heat dissipation were never good in the first place which makes the matter worst.
So this isn't just about Faster LLM performance. This could possibly opens up a whole new world for Mac ecosystem with CUDA.
> Apple's heat dissipation were never good in the first place
Apple has shipped many successful fanless systems - most recently the MacBook Air.
Apple's metal laptop cases (starting with the titanium PowerBook G4 in 2001) look nice and also help with heat dissipation - though the case itself can heat up. Thankfully shutdowns or burns due to overheating were (and are) rare.
Recently ThinkPads (for example) seem to have more thermal issues, but YMMV.
As someone who owns an insignificant midrange gaming build and has been irritated at the inflation of GPU prices with crypto and now genAI, I’m glad someone is.
They're still trying. I mean, not succeeding, but they're making some sort of effort. (On which note... If anyone's figured out how to run ollama/llamafile/... on any of the apparently numerous libraries that supposedly accelerate AI on Intel hardware please share any hints because supposedly I've got the hardware but I can't make sense of any of the docs...)
Most people who know about GPUs don't know even know what an H100 is. It's a data center GPU who almost no one will ever even touch, see or hear for their entire life, working on something that currently has lower impact then gaming. I'm not going to buy an intel GPU. But just to lay some pressure on the evil overlord that is Nvidia is very exciting.
Not everyone is willing to pay for 4K, 120 Hz, and quality raytracing. There is a market for less than premium experiences. I wanted some marginal CUDA for Blender, so I got a 3060. This experience is anything but insignificant to me.
Probably not, but Apple and NVIDIA collaborating on anything is news. They’re two companies that both want to be the ones wearing the Big Boy pants in any relationship so when they did anything together in the past, it hasn’t worked out for long.
Convinced that Apple has shot themselves in the foot / tied their hands behind their back / insert analogy here, re: privacy <> AI.
Interesting to see how it plays out. Meta and Google have much more permissive privacy policies / stances, which means Meta + Google models are going to get much better faster.
Apple does potentially have an edge with their Mx series of chips re: inference flops. I bet they're hoping that the model quality vs. model size curve continues dropping such that they can run sufficiently powerful LLMs on-device.
a) Apple already reaches out to ChatGPT (and soon to be others) for the full LLM experience. And there isn’t much benefit to be had from building their own competitor.
b) Privacy is essential if you want to an LLM to interact with you with private information. You can’t just upload unencrypted photos, messages, health data etc to the cloud where it will be accessible by any government with a search warrant. And so it does makes sense to keep it all private and encrypted on device or used in private but restricted compute.
c) Hype is very much coming out of the AI space and so I don’t see the market punishing Apple for not doing more. If anything they should’ve done nothing rather than released the technically impressive but largely useless Apple Intelligence product.
Apple has a huge legacy burden of defaulting laptops to 2010 levels of memory capacity at 8GB all the way up to mid 2024. All the great AI inference stuff in the M-series is wasted due to that until you get ~80% penetration of the new 16GB min spec.
If they get compelling local inference throughout the OS it may help them drive adoption of new models faster, but App devs won't have incentive to integrate local AI stuff until there is more market penetration of the higher RAM capacities. Apple could perhaps monetarily incentivise them to make up for it. But I think they are just going to heavily have to lean on cloud for everything.
And not sure there are many use cases for running mid-sized models locally. Smaller models fit in 8GB and work fine for action handling, tagging, classification etc. And for text/image generation you just get such a better user experience using a cloud hosted model.
Complete speculation but could be that Apple supports these operators on their GPUs and they need end-user models optimized to use them. Apple makes their money on consumer devices and need them to be performant for inference on the edge. Getting NVIDIA to integrate the operators may result in more models optimized for operators Apple's devices can leverage to stay ahead of other edge GPUs.
