- TinyCorp defies expectations by enabling Nvidia GPU to run 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 in Apple MacBooks was considered unviable by developers and hardware enthusiasts alike.
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 are based on a built-in iGPU, eliminating the need for external GPU support in macOS.
Apple’s hardware design made GPU integration difficult
Developers and enthusiasts have long tried to bridge the gap by creating their own controllers, but success was limited and often unreliable.
TinyCorp, a small artificial intelligence 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 running on Apple Silicon over USB3, has now made Nvidia GPUs work on M-series MacBooks over USB4 and Thunderbolt 4 connections.
Although TinyCorp has not detailed the full technical process, its success will likely depend on the use of native PCIe support and the higher bandwidth offered by USB4 and Thunderbolt 4.
These standards were designed for high-performance peripherals like GPU docks, giving developers a cleaner path than the old 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 via a USB4 dock.
Still, there are important limitations. The drivers developed by TinyCorp are designed specifically for AI workloads rather than gaming or screen rendering.
Users cannot expect the external GPU to drive a monitor or accelerate macOS graphics.
Instead, the focus is on enabling compute-intensive AI tasks, which could be transformative for developers who rely on local resources.
This achievement has direct implications for those working with LLM 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 data sets or train models locally instead of relying entirely on cloud or data center environments.
Such flexibility could make Apple laptops more relevant in AI research and machine learning experimentation, although for now it remains a niche use case.
TinyCorp’s work is impressive and combining Apple hardware with Nvidia GPUs in any capacity is an achievement many thought would never happen.
However, its reliance on custom drivers and external foundations means that the long-term viability of this solution remains to be seen.
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