TinyCorp shocks the tech world as Apple MacBooks run Nvidia RTX GPUs through a clever USB4 workaround
- 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 workable GPU pathway
For many years, the idea of running Nvidia GPUs on Apple MacBooks was considered unfeasible by both developers and hardware enthusiasts.
Apple’s decision to move away from Intel processors and fully embrace its ARM-based M-series chips meant 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 attempted to bridge the gap by crafting their own drivers, but success was limited and often unreliable.
TinyCorp, a small AI startup, has now found a practical path 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 getting Nvidia GPUs to operate 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 were designed for high-throughput peripherals like GPU docks, giving developers a cleaner route than the older USB3 interface.
The company’s post on X showed a MacBook Pro M3 Max running its open-source Tinygrad framework on an external Nvidia GPU through a USB4 dock.
Still, there are important limitations. The drivers TinyCorp developed are meant specifically for AI workloads rather than gaming or display rendering.
Users cannot expect the external GPU to drive a monitor or accelerate macOS graphics.
Instead, the focus is on enabling computation-heavy 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 demand high GPU power.
By pairing Nvidia’s RTX 30, 40, or 50 series GPUs with MacBooks, developers can handle larger datasets or train models locally rather than depending entirely on cloud or data center environments.
Such flexibility could make Apple’s laptops more relevant in AI research and machine learning experimentation, although this remains a niche use case for now.
TinyCorp’s work is impressive, and pairing Apple hardware with Nvidia GPUs in any capacity is an achievement that many thought would never happen.
However, its dependence on custom drivers and external docks means that the long-term practicality of this solution remains to be seen.
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