[LeanderK]

well, but machine learning is the major reason we use GPUs in the datacenter (not talking about consumer GPUs here). The others are edge-cases for data-centre applications! Apple is uniquely positioned exactly because it is already solved due to a significant part of the ML-engineers using MacBooks to develop locally.

The code to run these things on apples GPUs exist and is used every day! I don't know anyone using AMD GPUs, but pretty often its nvidia on the cluster and Apple on the laptop. So if nvidia is making these juicy profits, i think apple could seriously think about moving to the cluster if it wants to.

[talldayo]

Software developers using Macbooks doesn't mean Apple solved the ML problem. The past 10 years of MacOS removing features has somewhat proved that software developers will keep using Macs even when the featureset regresses. Like how Apple used to support OpenCL as a CUDA alternative, but gave up on it altogether to focus on simpler, mobile-friendly GPU designs.

The Pytorch MPS patches are a fun appeasement for developers, but they didn't unthrone Nvidia's demand. They didn't beat Nvidia on performance per watt, they didn't match their price, their scale or CUDA's featureset, and they don't even provide basic server drivers. It's got nothing to do with what brand you prefer and everything to do with what makes actual sense in a datacenter. Apple can't take on Nvidia clusters without copying Nvidia's current architecture - Apple Silicon's current architecture is too inefficient to be a serious replacement to Nvidia clusters.

If Apple wanted to have a shot at entering the cluster game, that window of opportunity closed when Apple Silicon converged on simplified GPU designs. The 2w NPUs and compute shaders aren't going to make Nvidia scared, let alone compete with AMD's market share.