周一，亚马逊云服务（AWS）发布了一款名为TorchNeuron的工具，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。此举也是亚马逊对英伟达软件优势的直接挑战——正是这一优势帮助英伟达成为AI应用的默认硬件选择，并推动其崛起为AI硬件的首选供应商。
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 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.
TorchNeuron可与PyTorch配合使用，后者是开发者用来构建、训练和运行AI模型的免费开源工具包。据亚马逊介绍，它让用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。
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.
亚马逊表示，约85%的AI应用是用PyTorch编写的。PyTorch最初由Facebook开发，但自2022年起归属Linux基金会，该基金会负责维护开源项目。英伟达多年来一直致力于确保其CUDA软件——即让应用能够在英伟达GPU及其他硬件上运行的软件基础设施——无缝支持用PyTorch编写的AI应用。结果，英伟达的硬件如今已深度嵌入该工具包，基本上成为默认选项。
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. 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改编自PyTorch，因此熟悉PyTorch的开发者已经知道如何使用它。亚马逊并非第一家试图轻松将自家芯片接入该平台的公司。PyTorch用户同样可以通过简单的配置更改，将应用移植到AMD的GPU甚至苹果的芯片上。
TorchNeuron is adapted from PyTorch, so developers who know PyTorch already know how to use it. 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.
不过，亚马逊主张，移植到Trainium不仅简单，还能让AI初创公司使用亚马逊的整个云服务。不出所料，AWS声称，对初创公司而言，这比使用新型云服务商（neoclouds）是更好的选择，后者中有许多与英伟达及其硬件绑定，例如Coreweave、Nebius和Firmus。
However, Amazon argues that porting to Trainium isn’t just easy; 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.
AWS安纳普尔纳实验室（负责制造亚马逊定制芯片的部门）技术软件副总裁科林·布雷斯通过电子邮件对TechCrunch表示：“你是在AWS上构建，而不是押注于单一用途的计算提供商，然后手忙脚乱地在它周围搭建其他一切。”“在AWS上，”他继续说道，“从原型到生产所需的基础设施已经就绪，让创始人能够快速推进，并在需要时扩展规模。”
“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.”
AWS表示，由于同一款芯片既处理训练（教AI模型），也处理推理（让训练好的模型投入使用，例如回答用户的问题），并且与初创公司所需的云基础设施紧密集成，因此即使AWS不提供最低的每token价格，其云服务也能为客户省钱。
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.
“Trainium的价值在于性价比，再加上它是训练和推理合一的平台，这改善了整个工作负载的经济性，”布雷斯说。“看看Deepgram——他们使用原生PyTorch迁移到了Trainium，获得了支持，并且以生产级准确度每美元处理的音频量提高了70%。”Deepgram是一家AI语音应用提供商，是ElevenLabs的竞争对手。
“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.
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便捷选项。
But Amazon isn’t content to just give PyTorch app developers an easy option to move to its cloud.
亚马逊还表示，它正在推动PyTorch减少自动以英伟达为中心的做法，并将把实现这一目标的代码贡献给PyTorch主项目。在宣布这一新工具的博客文章中，该公司写道：亚马逊的这一举措并不仅限于其自身的硬件产品。该公司还在积极推动 PyTorch 框架的改进，与 PyTorch 社区合作，移除那些将 PyTorch 与特定芯片绑定在一起的代码。
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. The goal is to make PyTorch more hardware-agnostic for the entire developer community, not just Trainium users.”
其目标是让整个开发者社区都能更轻松地使用 PyTorch，而不仅仅是 Trainium 的用户。虽然这种做法无疑对那些销售与 Nvidia 竞争的硬件和云服务的公司有利，但 PyTorch 的核心优势之一恰恰在于能够隐藏让 AI 代码在硬件上运行的所有复杂问题（即减少开发过程中的技术障碍）。我们还需要拭目以待：亚马逊的这些努力是否真的能为开发者提供更多选择和更实惠的解决方案（正如亚马逊所承诺的那样），还是会带来更多的配置难题。
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.
无论如何，如果亚马逊成功地将 PyTorch 与 Nvidia 的硬件平台彻底解耦，这将对 Nvidia 这家 GPU 巨头产生深远的影响——其影响远不止于应对竞争对手。正如 Brace 所指出的：“真正的意义在于消除那些限制开发者自由选择的因素。”
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.”