---
title: "According to reports, Google is researching to make TPU more compatible with the AI software framework PyTorch in an effort to become a viable alternative to NVIDIA GPUs"
type: "News"
locale: "en"
url: "https://longbridge.com/en/news/270084129.md"
description: "Google is promoting the \"TorchTPU\" program to make its TPU chips more compatible with PyTorch, aiming to become a viable alternative to NVIDIA GPUs. Google is considering open-sourcing some software to accelerate customer adoption. PyTorch is an open-source project supported by Meta, and since its release in 2016, it has been closely associated with NVIDIA's CUDA software"
datetime: "2025-12-18T00:58:36.000Z"
locales:
  - [zh-CN](https://longbridge.com/zh-CN/news/270084129.md)
  - [en](https://longbridge.com/en/news/270084129.md)
  - [zh-HK](https://longbridge.com/zh-HK/news/270084129.md)
---

> Supported Languages: [简体中文](https://longbridge.com/zh-CN/news/270084129.md) | [繁體中文](https://longbridge.com/zh-HK/news/270084129.md)


# According to reports, Google is researching to make TPU more compatible with the AI software framework PyTorch in an effort to become a viable alternative to NVIDIA GPUs

According to a report by Reuters, Google, a subsidiary of Alphabet (GOOGL.US), is promoting a project codenamed "TorchTPU" to make its Tensor Processing Unit (TPU) artificial intelligence (AI) chips better compatible with PyTorch, the most widely used AI software framework globally, and fully compatible with customers who have built their technology architecture based on PyTorch, becoming a viable alternative to NVIDIA (NVDA.US) graphics processing units (GPUs). The report indicates that Google is also considering open-sourcing some software to accelerate customer adoption.

PyTorch is an open-source project supported by Meta Platforms (META.US) and is one of the most commonly used tools by engineers developing AI models. The project is a set of pre-written code libraries and frameworks that can automate many common tasks in AI software development. Since its release in 2016, PyTorch's development has been closely tied to NVIDIA's CUDA software, which the market views as NVIDIA's strongest moat against competitors

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