**TinyCorp Defies Expectations by Enabling Nvidia GPU Operation on Apple Silicon**
Developers can now run heavy AI workloads locally on MacBooks equipped with Nvidia RTX cards, thanks to an innovative breakthrough by TinyCorp. This small AI startup has unlocked a practical method to connect Nvidia GPUs to Apple’s ARM-based M-series chips, a feat long considered unfeasible.
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### A New GPU Pathway for Apple Devices
Apple’s transition away from Intel processors to its own ARM-based M-series chips marked the end of official driver support for Nvidia and AMD GPUs on macOS. Since these new chips include built-in integrated GPUs (iGPUs), Apple’s hardware design has largely removed the need for external GPU compatibility, making it challenging to incorporate Nvidia cards.
Developers and hardware enthusiasts have attempted for years to create their own drivers and workarounds, but these efforts often resulted in limited and unreliable success.
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### How TinyCorp Made It Possible
Although TinyCorp has not shared the full technical details of its solution, the breakthrough likely hinges on leveraging the native PCIe support and higher bandwidth capabilities offered by USB4 and Thunderbolt 4 interfaces. These modern standards were designed to support high-throughput peripherals such as GPU docks, providing a cleaner and more efficient route compared to older USB3 connections.
In a recent post on X, TinyCorp demonstrated a MacBook Pro M3 Max running its open-source Tinygrad AI framework on an external Nvidia GPU connected via a USB4 dock, showcasing the practical use of this setup.
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### Important Limitations to Consider
It’s important to note that the custom drivers TinyCorp developed are focused specifically on AI computation workloads. Users should not expect this external GPU setup to support gaming, display rendering, or acceleration of general macOS graphics. The external Nvidia GPU cannot drive a monitor but excels at computation-heavy AI tasks.
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### Implications for AI Development on MacBooks
This advancement could be transformative for developers working with large language models (LLMs) and other AI tools requiring substantial GPU power. By pairing Nvidia’s RTX 30, 40, or 50 series GPUs with MacBooks, it becomes possible to handle larger datasets and train AI models locally—reducing dependence on cloud or data center resources.
While this remains a niche use case for now, it broadens the relevance of Apple laptops in AI research and machine learning experimentation.
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### Looking Ahead
TinyCorp’s achievement represents an impressive milestone—combining Apple hardware with Nvidia GPUs was once thought impossible by many. However, the solution’s reliance on custom drivers and external docks means its long-term practicality and widespread adoption remain uncertain.
Stay tuned to TechRadar for more updates on this exciting development.
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https://www.techradar.com/pro/security/apple-macbooks-running-nvidia-rtx-gpus-are-not-a-fantasy-anymore-tiny-corp-unlocks-a-whole-new-world-of-possibilities-in-a-surprisingly-low-tech-way