- TinyCorp defies expectations by enabling Nvidia GPU operation on Apple silicon
- Developers can now run heavy AI workloads locally on MacBooks with RTX cards
- USB4’s native PCIe support finally gave Apple devices a viable GPU path
For many years, the idea of running Nvidia GPUs on Apple MacBooks was considered impossible by developers and hardware enthusiasts alike.
Apple’s decision to move away from Intel processors and fully embrace its ARM-based M-series chips marked the end of official driver support for Nvidia and AMD.
These chips rely on a built-in iGPU, removing the need for external GPU compatibility on macOS.
Apple’s hardware design made GPU integration difficult
Developers and enthusiasts have long tried to bridge the gap by making their own drivers, but success was limited and often unreliable.
TinyCorp, a small AI startup, has now found a practical way forward after years of failed attempts by others.
The company known for building the world’s first external AMD GPU to run on Apple Silicon via USB3 has now succeeded in making Nvidia GPUs work on M-series MacBooks through USB4 and Thunderbolt 4 connections.
Although TinyCorp has not detailed the full technical process, its success likely depends on using the native PCIe support and higher bandwidth offered by USB4 and Thunderbolt 4.
These standards are designed for high-capacity peripherals like GPU docks, giving developers a cleaner route than the legacy USB3 interface.
The company’s post at X showed a MacBook Pro M3 Max running its open-source Tinygrad framework on an external Nvidia GPU through a USB4 docking station.
Still, there are important limitations. The drivers that TinyCorp developed are specifically intended for AI workloads rather than gaming or screen rendering.
Users cannot expect the external GPU to drive a display or accelerate macOS graphics.
Instead, the focus is on enabling computationally intensive AI tasks, which could be transformative for developers who rely on local resources.
This achievement has direct implications for those working with LLMs and other AI tools that require high GPU power.
By pairing Nvidia’s RTX 30-, 40-, or 50-series GPUs with MacBooks, developers can handle larger data sets or train models locally instead of relying entirely on cloud or data center environments.
Such flexibility could make Apple’s laptops more relevant in AI research and machine learning experiments, though this remains a niche use case for now.
TinyCorp’s work is impressive, and pairing Apple hardware with Nvidia GPUs in any capacity is a feat many thought would never happen.
But its reliance on custom drivers and external docks means the long-term practicality of this solution remains to be seen.
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