周二，谷歌宣布其备受期待的 PyTorch 版本——TorchTPU——现已作为开源项目正式发布。此举使谷歌加入了亚马逊的行列，共同试图打破 Nvidia 在人工智能（AI）行业中的垄断地位。目前，大多数 AI 应用程序默认都是为在 Nvidia 的芯片（即 GPU）上运行而设计的。
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.
PyTorch 是一款广受欢迎的开源工具包，被 AI 应用程序开发者和初创企业用于构建、训练和运行 AI 模型。据称，85% 的 AI 应用程序都在使用 PyTorch。该工具包最初由 Meta（当时的 Facebook）于 2016 年开发，当时人们还远未意识到大型语言模型（LLMs）会对未来产生深远影响。由于 Nvidia 的 GPU 当时已是 AI 研究的标准硬件，因此 PyTorch 的许多功能都是专门为与 Nvidia GPU 兼容而设计的。
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. 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.
2022 年，Meta 将 PyTorch 交给了 Linux 基金会（一个负责管理众多重要开源项目的非营利组织）。
In 2022, Meta turned PyTorch over to the Linux Foundation, a nonprofit that manages many important open source projects.
谷歌推出的 TorchTPU 版本允许 AI 应用程序在谷歌自主研发的 AI 芯片（称为 Tensor Processing Units，简称 TPUs）上运行。此外，它还简化了将现有 PyTorch 应用程序迁移到 TPUs 以及谷歌云平台（Google Cloud）的过程：开发者只需进行简单的配置调整即可，无需重新编写代码。
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.
周一，亚马逊也发布了类似的声明，推出了自己的 PyTorch 版本——TorchNeuron。TorchNeuron 可让 AI 应用程序直接移植到亚马逊自研的 AI 芯片 Trainium 上，并在无需重新编写代码的情况下运行在 AWS 平台上。亚马逊还表示，他们将进一步努力削弱 Nvidia 对 PyTorch 的控制力：他们正在向 PyTorch 的核心代码库提交代码，以分离那些依赖于 Nvidia 芯片的特定功能，从而使 PyTorch 更能适应各种不同的硬件平台。
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.
不过，在某些方面，谷歌可能略胜一筹。谷歌表示，他们与 Meta、Hugging Face、Inferact（vLLM 的开发公司）、RadixArk（SGLang 的开发公司）等合作伙伴进行了密切合作，确保 TorchTPU 的各项功能及代码库能够像原版 PyTorch 一样，与这些流行的 AI 开发工具完美兼容。
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.
此外，这家搜索巨头还推出了专用工具 TPU Agent Suite，帮助开发者将应用迁移到 Google 的芯片和云端，并在过程中修复错误。它甚至更新了广受欢迎的 LiteRT 工具——该工具可在手机、笔记本电脑和嵌入式设备上运行 AI——使其能更好地兼容 PyTorch AI 应用。
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.
这一切并不意味着 AI 应用开发者会仅仅因为现在可以轻松迁移到替代芯片和云端，就自动大规模弃用 Nvidia 的硬件。但亚马逊和 Google 显然正在努力终结 Nvidia 的优势。
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.