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Google and AWS open new paths for PyTorch beyond Nvidia

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Google and AWS open new paths for PyTorch beyond Nvidia

At PyTorch Conference North America 2026 this week, Google open-sourced TorchTPU, a native PyTorch stack for Google TPUs, now available on GitHub. AWS also launched TorchNeuron in public beta, its open-source native PyTorch backend for AWS Trainium, also available on GitHub.

Both releases seem aimed at making it easier for developers to move beyond Nvidia GPUs, whose CUDA software ecosystem has long dominated AI development. But while Google and AWS’s new offerings do ease some of the challenges of switching hardware, getting top-tier performance still isn’t straightforward. 

For developers eager for more computing options, it’s promising but somewhat disappointing.

Google put TorchTPU in preview with select customers in April (PyTorch says Google’s been working on the project since mid-2025), teasing a simpler way to run PyTorch workloads on Google’s AI chips. PyTorch support has long been an awkward dance, so the new backend could make it easier to run existing models on TPUs.

Google says it wants to make its TPU software ecosystem more open and developer-friendly for the PyTorch community, as it works to make the chips a more practical option for developers already using PyTorch. 

Developed in collaboration with the PyTorch team at Meta, TorchTPU allows developers to run existing PyTorch workloads without JAX dependencies in the runtime. This way, rather than requiring extensive code changes, developers can migrate existing workloads with only “trivial code changes,” according to PyTorch. 

compile to optimize TPU utilization and reduce memory overhead.



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