Startups and enterprises in robotics, video, world models, self-driving cars, speech, drug discovery, LLMs, and finance are already building on it.

Today, 85% of AI researchers and developers write their code in PyTorch. Until now, the hardware that code could run on was far more limited than the code itself, and expanding to new chips meant rewriting and revalidating work that took months to perfect.

Amazon is now closing that gap. With TorchNeuron , the PyTorch code developers already write runs natively on Amazon's AWS Trainium chips , with a single configuration change and no rewrite. TorchNeuron will be released under an open-source license at the PyTorch Conference North America on October 20.

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Think of your AI code as a car you've spent years fine-tuning. The engine, the suspension—every part working together. A new, more affordable engine comes along with serious horsepower, but installing it would mean tearing down the car and rebuilding it piece by piece. TorchNeuron lets you swap in the new engine without taking the car apart.

In a side-by-side demonstration, Amazon engineers ran the same AI training script on a conventional chip and on Trainium . The only difference was a setting that told the code which chip to use. Results showed that the AI model learned the same things at the same rate.

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