This week, a buzz rippled through Reddit's local AI community: a developer used an unofficial driver to squeeze an NVIDIA RTX 5060 GPU into macOS 15 and got Metal 3 acceleration working — the first time grassroots developers have meaningfully pried open Apple's lock on its own hardware ecosystem.
What this is
Let's clarify a few things first. By "unofficial driver," we mean a patch that neither Apple nor NVIDIA has shipped — no guarantee of stability, no compliance cover. The RTX 5060 is NVIDIA's mid-range desktop card; macOS has never supported NVIDIA GPUs. And Metal 3 is Apple's own GPU acceleration API — letting a third-party GPU borrow Apple's graphics scheduling system is, on its face, a tough technical nut to crack.
Until now, running local AI on macOS essentially meant two routes: either stick with Apple Silicon (the M-series chips Apple designs in-house) and Apple's MLX framework, or dual-boot into Linux/Windows to enjoy the maturity of NVIDIA's CUDA (its parallel computing platform). This hack effectively opens a third path.
Industry view
The optimistic camp: local AI players no longer have to agonize over "buy Apple or buy NVIDIA." If this stabilizes, a macOS that runs both MLX and CUDA ecosystems in parallel could arrive sooner than we think.
But the cautious camp is also flagging concerns: unofficial drivers mean uncontrolled behavior. This kind of breakthrough is more a developer tinkerer's experiment — far from anything you'd put in a production environment. Apple's continued refusal to open NVIDIA support is, at bottom, a commercial strategy. The AI compute on M-series silicon is one of the very few things still propping up the Mac's price premium.
In other words: grassroots developers getting it running, and ordinary users getting it running stably, are two different things.
Impact on regular people
For enterprise IT: essentially no impact today. Unofficial drivers won't make it onto procurement lists or pass compliance review.
For individual professionals: if your daily toolchain is Mac and you run local large-model experiments, stick with MLX on Apple Silicon — it's the most stable path. Don't bet on the unofficial route.
For the consumer market: this incident indirectly underscores one fact — Apple cares deeply about its AI hardware moat. If chips like the M5 Ultra actually ship, marketing will almost certainly keep leading with "AI inference performance."