How Google’s TPUs Are Challenging Nvidia: The Future of AI Chips Explained

For years, Nvidia has been the king of AI hardware, thanks to its powerful graphics processing units (GPUs). These chips became the backbone of the AI boom because they can perform thousands of calculations at the same time. But now, Nvidia’s lead may be at risk. A new wave of specialised chips — especially Google’s Tensor Processing Units (TPUs) — is gaining massive attention. Reports even suggest that companies like Meta and Anthropic are ready to invest billions in Google’s TPU technology.

So, what exactly is a TPU, and why is it suddenly so important?

What Is a TPU?

Most AI progress over the last decade has relied on GPUs, originally created for gaming and graphics. GPUs are great at handling many tasks in parallel — such as rendering millions of pixels at once. This ability also happens to be perfect for AI models, which require huge amounts of matrix multiplication (large grids of numbers multiplied simultaneously).

But GPUs were never built specifically for AI. They were adapted for it.

This is where TPUs come in.

Google introduced the first TPU in 2016. Unlike GPUs, TPUs are purpose-built for AI workloads. Their entire design focuses on executing matrix operations efficiently — the core calculation needed for training and running large models like Gemini, AlphaFold, and many other deep-learning systems.

In 2024, Google launched its seventh-generation TPU, called TPU v7 “Ironwood.” This chip powers Google’s most advanced AI tools and is now being offered to outside companies at scale.

Are TPUs Better Than GPUs for AI?

Experts say that TPUs are not a completely different category of chip, but a more specialised version of GPUs. They focus on the parts of computation that matter most for AI.

Advantages of TPUs

• Higher efficiency for AI workloads
  Since TPUs are dedicated to matrix multiplication, they can perform AI training and inference faster and with less energy.

• Lower cost for large-scale training
  For huge AI models, TPUs can save companies tens or even hundreds of millions of dollars.

• Optimised for cloud-based AI
  Google designed TPUs to work in massive data centres, which makes them ideal for companies training enormous models.

Disadvantages of TPUs

• Less flexible
  GPUs can handle a wide range of tasks. TPUs are specialised, so if AI architectures change, TPUs may struggle without software updates.

• Initially harder to use
  Early TPUs lacked the simple developer tools that Nvidia offers. But this is changing fast — modern TPUs now offer user-friendly software similar to Nvidia’s CUDA ecosystem.

In short:
TPUs are faster and cheaper for many AI tasks, but GPUs remain more adaptable.

Who Is Building TPUs?

Google was the first to introduce TPUs, but now almost every major tech giant is creating its own specialised AI chips to reduce dependence on Nvidia.

These include:

• Amazon – Trainium and Inferentia chips

• Meta – Custom MTIA accelerators

• Microsoft, OpenAI, and Tencent – developing in-house AI processors

• Many AI hardware start-ups are emerging as well, supported by rising demand

The main reason?
Nvidia GPUs have become extremely expensive and hard to get, as global demand skyrockets faster than supply.

By designing their own AI chips, companies can reduce costs and control their hardware future.

How TPUs Could Change the AI Industry

One of the biggest shifts happening now is that major AI companies may finally be ready to switch from Nvidia to TPU-powered cloud services.

For years, Google mostly used TPUs internally for its own products. But now, companies like Meta and Anthropic are reportedly signing huge deals to buy TPU computing power.

If this trend continues, it could reshape the AI market in several ways:

1. More competition in AI hardware

Nvidia has been the only dominant supplier for a long time. TPUs give companies a strong alternative.

2. Lower prices

With more options available, companies can negotiate better deals. Even the threat of switching to TPUs may push Nvidia to offer more competitive pricing.

3. Faster AI innovation

Specialised chips built for AI may unlock new types of models that weren’t possible before.

Final Thoughts

GPUs will continue to play a major role in AI, but TPUs are becoming a powerful force. With companies like Meta and Anthropic investing heavily in Google’s TPU technology, the AI hardware landscape is entering a new era of competition. As TPUs mature and become easier to use, they may challenge Nvidia’s long-standing dominance — giving the AI industry more speed, more choice, and potentially much lower costs.