Google has joined Amazon in attacking Nvidia’s stronghold with AI app developers

On Tuesday, Google announced that TorchTPU, its long-awaited version of PyTorch, is now available as an open-source project. With this, Google joins Amazon in trying to break Nvidia’s stranglehold on the AI industry, where most AI apps are written by default to run on Nvidia’s chips, or GPUs.

周二，谷歌宣布其期待已久的 PyTorch 版本 TorchTPU 现已作为开源项目发布。至此，谷歌加入了亚马逊的行列，试图打破英伟达在人工智能行业的主导地位，因为目前大多数人工智能应用程序默认都是为在英伟达的芯片（即 GPU）上运行而编写的。

PyTorch is the popular open-source toolkit that AI app developers and startups use to build, train, and run AI models. It is, by some accounts, used by 85% of the AI apps. PyTorch was originally developed by Meta (then Facebook) in 2016, long before the world had an inkling that LLMs would change the world.

PyTorch 是人工智能应用开发者和初创公司用于构建、训练和运行人工智能模型的热门开源工具包。据一些统计，85% 的人工智能应用程序都在使用它。PyTorch 最初由 Meta（当时称为 Facebook）于 2016 年开发，那时世界还未预见到大语言模型（LLM）将改变世界。

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.

当时，英伟达的 GPU 已经是人工智能研究的标准硬件，因此 PyTorch 的许多功能都是专门为配合这些硬件而构建的。2022 年，Meta 将 PyTorch 移交给了 Linux 基金会，这是一个管理着许多重要开源项目的非营利组织。

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 re-writing of their apps.

谷歌的版本 TorchTPU 让 AI 应用程序能够在谷歌的 AI 芯片（即张量处理单元，TPU）上运行。它还使得将现有的 PyTorch 应用程序迁移到 TPU 以及谷歌云变得更加容易。开发者只需更改简单的配置，无需重写应用程序即可实现这一目标。

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 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.

亚马逊周一也发布了类似的公告，推出了其自己的 PyTorch 版本 TorchNeuron。TorchNeuron 允许将 AI 应用程序移植到亚马逊自研的 AI 芯片 Trainium 以及 AWS 上，且无需重写代码。亚马逊还表示，为了削弱英伟达对 PyTorch 的控制，他们正在采取更进一步的行动。该公司正在向 PyTorch 主项目提交代码，旨在将各种功能与英伟达芯片解耦，从而使其默认对任何硬件更加开放。

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.

然而，谷歌在某些方面可能比亚马逊更胜一筹。谷歌表示，它直接与 Meta、Hugging Face、Inferact（vLLM 的制造商）、RadixArk（SGLang 的制造商）等公司合作，确保 TorchTPU 的功能和代码库能像原始 PyTorch 一样，与这些流行的人工智能开发工具良好兼容。

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.

此外，这家搜索巨头还推出了一款专用工具——TPU 代理套件，旨在帮助开发者将其应用程序迁移到谷歌的芯片和云端，并在过程中修复错误。它甚至更新了其热门工具 LiteRT（用于在手机、笔记本电脑和嵌入式设备上运行 AI），使其能更好地支持 PyTorch AI 应用程序。

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.

这一切并不意味着人工智能应用开发者会仅仅因为现在可以轻松迁移到其他芯片和云平台，就集体放弃英伟达的硬件。但亚马逊和谷歌显然正在努力终结英伟达的优势地位。