If we know anything about Apple, they're going after Nvidia. If anyone can pull it off, it's going to be them.
You seem to be positioning this as a Ford vs Chevy duel, when (to me at least) the comparison should be to Ford vs Exxon.
Nvidia is an infrastructure company. And a darned good one. Apple is a user facing company and has outsourced infrastructure for decades (AWS & Azure being two of the well known ones).
The only way it hurts Nvidia is if Apple becomes the runaway leader of the pc market. Even then, Apple hasn’t shown any intent of selling GPUs or AI processors to the likes of AWS, or Azure or Oracle, etc.
Nvidia has a much bigger threat from with Intel/AMD or the cloud providers backward integrating and then not buying Nvidia chips. Again, no signs that Apple wants to do this.
If you're saying Apple is going to 'take on nvidia' in edge inference, then I don't disagree but I would hardly even count that as taking on nvidia.
It took almost a decade but the PA Semi acquisition showed that Apple was able to get out of the shadow of its PowerPC era.
Nvidia will remain a leader in this space for a long time. But things are going to play out wonky and Apple, when determined, are actually pretty good at executing on longer-term roadmaps.
I think Apple is going to make rapid and substantial advancements in on-device AI-specific hardware. I also think nVIDIA is going to continue to dominate the cloud infrastructure space for training foundational models for the foreseeable future, and serving user-facing LLM workloads for a long time as well.
For that they would have to develop servers that has a mass amount of whatever it is or sell the chips in the same manner Nvidia does today.
I dont see that future for Apple.
Microsoft / Google / or other major cloud companies would do extremely well if they could develop it and just keep it as a major win for their cloud products.
Azure is running OpenAI as far as I have heard.
Imagine if M$ made a crazy fast GPU/whatever. It would be a huge competitive advantage.
Can it happen? I dont think so.
I just don't understand how they can compete on their own merits without purpose-built silicon; the M2 Ultra doesn't shine a candle to a single GB200. Once you consider how Nvidia's offerings are networked with Mellanox and CUDA universal memory, it feels like the only advantage Apple has in the space is setting their own prices. If they want to be competitive, I don't think they're going to be training Apple models on Apple Silicon.
NASDAQ average P:E - 31
NVidia's P:E - 71
That's a market of 1 vendor. That's ripe for attack.
You see, I want to live in a world where GPU manufacturers aren't perpetually hostile against each other. Even Nvidia would, judging by their decorum with Khronos. Unfortunately, some manufacturers would rather watch the world burn than work together for the common good. Even if a perfect CUDA replacement existed like it did with DXVK and DirectX, Apple will ignore and deny it while marketing something else to their customers. We've watched this happen for years, and it's why MacOS perennially cannot run many games or reliably support Open Source software. It is because Apple is an unreasonably fickle OEM, and their users constantly pay the price for Apple's arbitrary and unnecessary isolationism.
Apple thinks they can disrupt AI? It's going to be like watching Stalin try to disrupt Wal-Mart.
That's entirely the fault of AMD and Intel fumbling the ball in front of the other team's goal.
For ages the only accelerated backend supported by PyTorch and TF was CUDA. Whose fault was that? Then there was buggy support for a subset of operations for a while. Then everyone stopped caring.
Why I think it will go different this time: nVidia's competitors seem to have finally woken up and realized they need to support high level ML frameworks. "Apple Silicon" is essentially fully supported by PyTorch these days (via the "mps" backend). I've heard OpenCL works well now too, but have no hardware to test it on.
it's just a monopoly [1] , how hard can it be?
/s
- [1] practically, because of how widespread cuda is
…though it took two solid decades to even make a dent in x86.