Amazon is taking its biggest shot at Nvidia yet with new TorchNeuron dev tool

On Monday, Amazon Web Services released a tool called TorchNeuron that lets AI startups easily move their AI apps onto Amazon’s AI chip, Trainium. In doing so, Amazon is also taking a direct stab at the software advantage that has helped Nvidia become the default hardware choice for AI apps and spurred its rise as AI hardware provider of choice.

周一，亚马逊云科技（AWS）发布了一款名为 TorchNeuron 的工具，旨在让 AI 初创公司能够轻松地将其 AI 应用迁移到亚马逊的 AI 芯片 Trainium 上。通过此举，亚马逊也直接挑战了英伟达的软件优势——正是这种优势帮助英伟达成为了 AI 应用的首选硬件，并推动其崛起为 AI 硬件领域的首选供应商。

TorchNeuron works with PyTorch, a free, open source toolkit that developers use to build, train, and run AI models. It lets AI apps written with PyTorch run on Trainium by changing just one line of configuration, according to Amazon — no code rewrite required. Amazon says about 85% of AI apps are written with PyTorch. Facebook originally developed it, but since 2022, it has been part of the Linux Foundation, which maintains open source projects.

TorchNeuron 适用于 PyTorch，这是一个开发者用于构建、训练和运行 AI 模型的免费开源工具包。据亚马逊称，它允许使用 PyTorch 编写的 AI 应用只需更改一行配置即可在 Trainium 上运行，无需重写代码。亚马逊表示，约 85% 的 AI 应用是使用 PyTorch 编写的。该框架最初由 Facebook 开发，但自 2022 年起，它已成为负责维护开源项目的 Linux 基金会的一部分。

Nvidia has spent years ensuring its CUDA software — the software infrastructure that allows apps to run on Nvidia GPUs and its other hardware — seamlessly supports AI apps written with PyTorch. As a result, Nvidia’s hardware is now deeply embedded in the toolkit, essentially by default. TorchNeuron is adapted from PyTorch, so developers who know PyTorch already know how to use it.

英伟达多年来一直致力于确保其 CUDA 软件（即允许应用在英伟达 GPU 及其它硬件上运行的软件基础设施）能够无缝支持使用 PyTorch 编写的 AI 应用。因此，英伟达的硬件现在已深度嵌入该工具包中，基本上成为了默认选项。TorchNeuron 是基于 PyTorch 改编的，因此熟悉 PyTorch 的开发者已经知道如何使用它。

Amazon isn’t the first company to attempt to easily bring their chips to the platform. PyTorch users can also port their apps to AMD GPUs or even Apple’s chips with a simple configuration change. However, Amazon argues that porting to Trainium is not only easy, but it also gives AI startups access to Amazon’s entire cloud. Unsurprisingly, AWS claims that this is a better choice for startups than using neoclouds, many of which are tied to Nvidia and its hardware, like CoreWeave, Nebius, and Firmus.

亚马逊并不是第一家尝试将其芯片轻松引入该平台的公司。PyTorch 用户也可以通过简单的配置更改，将应用移植到 AMD GPU 甚至苹果的芯片上。然而，亚马逊认为，移植到 Trainium 不仅简单，还能让 AI 初创公司接入亚马逊的整个云生态。不出所料，AWS 声称对于初创公司而言，这比使用 CoreWeave、Nebius 和 Firmus 等许多与英伟达及其硬件深度绑定的“新云”（neoclouds）服务是更好的选择。

“You’re building on AWS. You’re not betting on a single-purpose compute provider and scrambling to build everything else around it,” Colin Brace, VP of Technology Software at AWS Annapurna Labs (the unit that makes Amazon’s custom chips), told TechCrunch via email. “On AWS,” he continued, “the infrastructure to go from prototype to production is already there, allowing a founder to move fast and scale when they need to.”

“你是在 AWS 上构建应用。你不需要把赌注押在单一用途的计算提供商身上，也不必为了围绕它构建其他一切而手忙脚乱，”AWS Annapurna Labs（负责制造亚马逊定制芯片的部门）技术软件副总裁 Colin Brace 通过电子邮件告诉 TechCrunch。“在 AWS 上，”他继续说道，“从原型到生产的基础设施已经就绪，这让创始人能够快速行动，并在需要时进行扩展。”

AWS says that because the same chip both handles training (teaching the AI model) and inference (putting the finished model to work, as when it answers a user’s question), and is tightly integrated with the cloud infrastructure that startups need, its cloud will save them money, even if AWS doesn’t offer the cheapest per-token price.

AWS 表示，由于同一款芯片既能处理训练（教授 AI 模型），也能处理推理（让成品模型投入工作，例如回答用户的问题），并且与初创公司所需的基础设施紧密集成，因此即使 AWS 不提供最低的每 Token 价格，其云服务也能为他们节省成本。

“Trainium’s value is price-performance plus the fact that it’s one platform for both training and inference, which improves the economics across a full workload,” Brace said. “Look at Deepgram — they moved to Trainium using native PyTorch support and they’re processing 70% more audio per dollar at production-grade accuracy.” Deepgram is an AI voice app provider, a competitor to ElevenLabs.

“Trainium 的价值在于性价比，以及它是一个集训练和推理于一体的平台，这改善了整个工作负载的经济效益，”Brace 说。“看看 Deepgram，他们使用原生的 PyTorch 支持迁移到了 Trainium，在达到生产级准确率的同时，每美元处理的音频量增加了 70%。”Deepgram 是一家 AI 语音应用提供商，也是 ElevenLabs 的竞争对手。

But Amazon isn’t content to just give PyTorch app developers an easy option to move to its cloud. Amazon also says it is pushing PyTorch to be less automatically centered on Nvidia, and it will contribute the code to do that to the main PyTorch project. In the blog post announcing the new tool, the company wrote: Amazon’s approach extends beyond its own hardware. The company is contributing changes upstream to PyTorch itself, working with the PyTorch community to remove code that ties the framework to specific chips.

但亚马逊并不满足于仅仅为 PyTorch 应用开发者提供一个迁移到其云端的简便选项。亚马逊还表示，它正在推动 PyTorch 减少对英伟达的自动依赖，并将为此贡献代码到 PyTorch 主项目中。在宣布该新工具的博客文章中，该公司写道：亚马逊的方法不仅限于其自身的硬件。该公司正在向 PyTorch 本身贡献上游变更，与 PyTorch 社区合作，移除将该框架绑定到特定芯片的代码。

The goal is to make PyTorch more hardware-agnostic for the entire developer community, not just Trainium users. While such a tactic is undoubtedly good for those who are selling hardware and cloud services that compete with Nvidia, one of the points of PyTorch is to hide all the pesky difficulties of getting AI code to run on hardware. We’ll have to wait and see if pushing Nvidia out that far gives developers more choice and more affordable options, as Amazon promises, or just more configuration headaches.

其目标是使 PyTorch 对整个开发者社区而言更具硬件无关性，而不仅仅是针对 Trainium 用户。虽然这种策略对于那些销售与英伟达竞争的硬件和云服务的公司来说无疑是有利的，但 PyTorch 的核心意义之一在于隐藏让 AI 代码在硬件上运行的所有繁琐困难。我们拭目以待，看看将英伟达挤出这一领域是否真如亚马逊所承诺的那样，能为开发者提供更多选择和更实惠的方案，还是只会带来更多的配置麻烦。

Either way, if Amazon succeeds in deeply uncoupling PyTorch from Nvidia, this could have much bigger ramifications for the GPU giant than simply dealing with chip competitors. As Brace said: “The real story is removing the lock-in that kept developers from having a real choice at all.”

无论如何，如果亚马逊成功地将 PyTorch 与英伟达深度解耦，这对这家 GPU 巨头的影响可能远不止应对芯片竞争对手那么简单。正如 Brace 所言：“真正的重点在于消除那种让开发者根本无法做出真正选择的锁定效应。”