Google Joins Amazon in Challenge to Nvidia's AI Lead
Google has followed Amazon's lead by introducing a developer tool that enables PyTorch users to transition their artificial intelligence applications off Nvidia hardware.
Radwan AbdulhadiEditor and technology writer
Google Releases Open-Source TorchTPU
Google has joined Amazon in trying to [break Nvidia’s stranglehold](https://techcrunch.com/2026/10/19/amazon-is-taking-its-biggest-shot-at-nvidia-yet-with-new-torchneuron-dev-tool/) on the artificial intelligence industry, where the vast majority of applications are written by default to run on Nvidia graphics processing units. As [announced](https://cloud.google.com/blog/topics/ai-infrastructure/torchtpu-is-now-open-source-run-native-pytorch-on-tpus) on Tuesday, Google's long-awaited version of PyTorch, known as TorchTPU, is now available as an open-source project.
PyTorch serves as a popular open-source toolkit that startups and developers utilize to build, train, and run AI models. Estimates suggest that PyTorch is used by roughly 85% of AI applications. Meta originally developed PyTorch in 2016 before large language models became mainstream. Because Nvidia chips were already standard in AI research at that time, many PyTorch features were constructed to work specifically with Nvidia hardware. Meta later transferred PyTorch to the Linux Foundation in 2022.
Migrating PyTorch Apps to Google TPUs
With Google’s new version, developers can run AI applications on Google’s specialized Tensor Processing Units. TorchTPU enables a smooth transition of existing PyTorch apps directly onto TPUs and Google Cloud without requiring a complete rewrite of the application code. Developers can achieve this transition simply by making a configuration change.
This rollout follows a [similar announcement Monday,] when Amazon introduced its own version of PyTorch called TorchNeuron. That tool allows applications to be ported to Amazon's Trainium chips and AWS without code rewriting. Furthermore, Amazon submitted code to the main PyTorch project to decouple features from Nvidia chips, making the framework more accessible to alternative hardware.
Ecosystem Collaboration and Additional Tools
Google emphasized that it collaborated directly with several industry partners and organizations, including Meta, Hugging Face, Inferact, and RadixArk, to ensure that TorchTPU libraries function seamlessly with popular development tools in the same manner as the original PyTorch framework.
In addition to TorchTPU, the search giant introduced the [TPU agent suite](https://github.com/google-pytorch/tpu-agent-suite) to assist developers in migrating their workloads to Google chips while resolving errors. Google also updated its LiteRT tool, designed for running AI on phones, laptops, and embedded devices, to offer better compatibility with PyTorch AI applications.
Industry Implications for Nvidia
While these recent developer tools from major cloud providers make it significantly easier to transition away from Nvidia hardware, industry observers note that developers may not abandon Nvidia's ecosystem en masse. Nevertheless, the coordinated pushes by both Amazon and Google demonstrate a clear industry effort to challenge Nvidia's long-standing hardware dominance.
Sources
- TechCrunchGoogle has joined Amazon in attacking Nvidia’s stronghold with AI app developers
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