Google is back in the spotlight in Silicon Valley—not because of its search engine or Android, but because of its fast progress in AI hardware. The company’s custom AI chips, called Google’s TPUs (Tensor Processing Units), are now in their seventh generation and are becoming serious competitors to Nvidia’s GPUs. With companies like Meta and Anthropic showing growing interest in these chips, the AI hardware market is seeing major changes.

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How Google’s TPU Journey Began

Google started working on TPU in 2013. At that time, training large AI models using normal computer chips like CPUs and GPUs was expensive and slow. TPUs were designed as special-purpose chips made only for handling machine learning tasks, especially matrix and tensor operations used in deep learning.

Over the years, Google improved the design and released new versions. The latest model, called TPUv7 or Ironwood, is the most powerful so far. In the early years, TPUs were only used inside Google for services like Google Search, Gmail, and YouTube. They were not easy for outside companies to use because of limited software support.

A New Strategy: Selling TPUs to the Industry

The rise of generative AI—especially after ChatGPT—changed Google’s approach. Instead of using TPUs only for internal services, Google now offers them to other companies as cloud-based AI hardware. The company now markets TPUs as high-performance yet cost-effective chips for training and running large AI models.

This shift has made big companies look closely at Google’s TPUs. Anthropic already uses them, and Meta is currently in advanced talks to buy large numbers of TPU systems. If Meta continues with this plan, it would be a major shift from its long history of using mostly Nvidia GPUs.

Anthropic’s use of multiple types of chips also shows that companies are no longer fully dependent on Nvidia. This has brought fresh competition to the AI chip market.

How This Affects Nvidia

News of Meta exploring TPU usage led to a drop in Nvidia’s stock price, showing how sensitive the market is to competition. Nvidia currently dominates AI hardware and earns a large share of its data centre revenue from large tech companies, also known as hyperscalers. If these companies switch to custom chips like Google’s TPUs, Nvidia’s position could weaken.

However, Nvidia still has advantages. It offers a complete ecosystem of software and tools, especially its platform called CUDA, which makes it easy for developers to train AI models. Many AI projects are already built on CUDA, and switching to a new system may take time.

Even though Google’s latest TPU architecture, Ironwood, offers performance close to Nvidia’s Blackwell GPUs, Nvidia remains ahead in flexibility, tools, and ecosystem maturity.

Key Facts in Simple Form

Google introduced the first TPU in 2015.
The newest version, TPUv7 (Ironwood), is designed for extremely large AI workloads.
Meta may start using TPUs from 2026, with full deployment expected in 2027.
Nvidia still leads in software support, especially for diverse AI applications.
Google’s TPUs are specially built for deep learning and tensor calculations.

What the Future of Google’s TPUs

Experts believe that even though Google’s TPUs are gaining momentum, GPUs will continue to play a major role. GPUs are easy to access across many cloud platforms and allow fast development cycles. Nvidia also claims it stays one full generation ahead in system design, even as Google improves its silicon performance.

The future of AI hardware may not be won by just one type of chip. Instead, competition between general-purpose GPUs and specialised AI chips like Google’s TPUs is expected to grow stronger. As more companies build large AI models, demand for faster, cheaper, and more efficient hardware will continue to rise.