Google and Amazon Both Take Aim at Nvidia's AI Developer Stronghold
On Tuesday, Google announced that TorchTPU, its long-awaited version of PyTorch, is now available as an open-source project. With this move, Google joins Amazon in attempting to break Nvidia's stranglehold on the AI industry, where most AI applications are written by default to run on Nvidia's chips, or GPUs.
Why PyTorch Matters
PyTorch is the popular open-source toolkit that AI app developers and startups use to build, train, and run AI models. By some accounts, it is used by 85% of AI applications. PyTorch was originally developed by Meta (then Facebook) in 2016, long before the world had any inkling that LLMs would change the world. At the time, Nvidia's GPUs were already the standard hardware for AI research, so many of PyTorch's features were built to work specifically with them. In 2022, Meta turned PyTorch over to the Linux Foundation, a nonprofit that manages many important open-source projects.
TorchTPU: Running PyTorch on Google's Chips
Google's version, TorchTPU, lets AI applications 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 applications.
Amazon's Countermove: TorchNeuron
Amazon made a similar announcement Monday when it released its own version of PyTorch, called TorchNeuron. TorchNeuron allows AI applications to be ported to Amazon's homegrown AI chip, Trainium, and to AWS with no rewriting. Amazon also said it is 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 the toolkit more open to any hardware by default.
Where Google May Have an Edge
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 applications.
The Road Ahead
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.
via TechCrunch AI