Google’s version, TorchTPU, lets AI apps run on Google’s AI chips, called Tensor Processing Units (TPUs). It also makes it easy to move existing PyTorch apps to TPUs and, therefore, to Google Cloud. Developers can do this with a simple configuration change and no rewriting of their apps.
Amazon made a similar announcement Monday, when it released its own version of PyTorch, called TorchNeuron. TorchNeuron allows AI apps to be ported to Amazon’s homegrown AI chip, Trainium, and to AWS with no rewriting. Amazon also said it’s going even further in its effort to loosen Nvidia’s hold on PyTorch. It is submitting code to the main PyTorch project that decouples various features from Nvidia’s chips, making it more open to any hardware by default.
Google may be one-upping Amazon in a few ways, however. It says it worked directly with Meta, Hugging Face, Inferact (maker of vLLM), RadixArk (maker of SGLang), and others to ensure that TorchTPU’s features and code libraries work well with these popular AI development tools, just as the original PyTorch does.
Plus the search giant has launched a dedicated tool, the TPU agent suite, to help developers move their apps to Google’s chips and cloud and fix errors along the way. It even updated its popular LiteRT tool, which runs AI on phones, laptops, and embedded devices, to work better with PyTorch AI apps.
None of this means AI app developers will automatically ditch Nvidia’s hardware en masse just because they can now easily move to alternative chips and clouds. But Amazon and Google are clearly working to end Nvidia’s advantage.
