Google TPU: The AI Inference Chip Dominating Cloud

Imagine this: back in 2013, a small group inside Google ran the numbers on voice search. If every Android user spoke to their phone for just three minutes a day, the company would literally have to double its entire global data-center footprint overnight. That single spreadsheet changed everything.

Instead of just buying more servers, Google did something almost no software company ever does – they decided to design their own silicon from scratch. Not for phones, not for laptops, but purely to survive the coming AI explosion. Ten years later, that decision looks like one of the smartest bets in tech history.

The Quiet Revolution Nobody Saw Coming

While the world obsessed over Nvidia’s gaming cards turned AI monsters, Google spent a decade perfecting something far more specialized. They call it the Tensor Processing Unit – the TPU. And right now, in 2025, the seventh generation just went live. The numbers leaking out from people who’ve actually used it are frankly ridiculous.

From Panic Project to Strategic Weapon

The story actually starts with a near-crisis. Google’s engineers realized standard CPUs and even GPUs were hopelessly inefficient for the giant matrix multiplications that power modern AI. Every time you ask Google Translate a question or get directions rerouted in Maps, that’s matrix math running billions of times per second.

Buying enough regular chips to keep up would have been financial suicide. So they built an Application-Specific Integrated Circuit – an ASIC – that does one thing and one thing only: run neural networks stupidly fast and cheap.

Here’s the crazy part – the first TPU was designed, taped out, and running real traffic in data centers in just fifteen months. For hardware, that’s basically warp speed. By 2015 they were already using them everywhere, and the outside world had no idea.

Why GPUs Are Carrying Too Much Baggage

Think about what a GPU was originally built for – drawing triangles really fast for video games. Yes, that parallel power works great for training AI models too, but it comes with overhead nobody needed for pure inference work.

• Complex caching systems for unpredictable game code
• Branch prediction for general-purpose workloads
• Texture units and rasterizers that sit completely unused during AI tasks
• Sophisticated scheduling for thousands of independent threads

The TPU throws all of that away. No graphics pipeline. No gaming heritage. Just raw, brutal efficiency at moving numbers through matrix multiplications.

The Systolic Array – Google’s Secret Sauce

The real magic happens in something called a systolic array. Picture a grid of thousands of tiny multipliers arranged like heart muscle cells. Data flows through this grid in rhythmic pulses – hence “systolic” – passing partially computed results directly to the next cell without ever going back to memory.

In a normal chip, you’d fetch data from memory, compute, write back, fetch again – over and over. That’s the famous Von Neumann bottleneck. The TPU largely eliminates it. Weights get loaded once, then inputs stream through like an assembly line. The savings in power and time are enormous.

“It’s like the difference between having a conveyor belt versus carrying each part individually across the factory floor every single time.”

– Former Google silicon engineer

The New King: TPU v7 “Ironwood”

Google revealed the seventh-generation TPU – codenamed Ironwood – earlier this year, and the specs are honestly hard to believe coming from a company that isn’t Nvidia.

Compared to the already impressive v5p that many customers still use:

Yes, you read that right – ten times the compute in just two generations. And remember, this is the chip that’s now running Gemini 3 internally at Google.

Real-World Numbers That Actually Matter

The performance claims sound great on paper, but what do people actually using these chips say?

“For the right workload, we see 1.4x better performance per dollar and significantly lower power draw. The heat difference alone is dramatic – entire rows of servers running cool enough that you can touch them.”

Another engineer who worked directly on search infrastructure told me the v6 generation was already 60-65% more efficient than Nvidia’s Hopper chips for their specific use case. With Ironwood, that gap has apparently widened further.

Perhaps most telling – even Nvidia’s CEO acknowledges Google is a “special case” among ASIC makers. When rumors surfaced that a major AI lab was testing Google TPUs for ChatGPT inference, Nvidia reportedly made urgent phone calls. That tells you everything about how seriously they take this threat.

The Ecosystem Elephant in the Room

So if TPUs are this good, why isn’t everyone using them?

The honest answer is painful: CUDA.

Every AI researcher learned CUDA in grad school. Every framework defaults to CUDA. Every startup builds on PyTorch with CUDA backend. Switching to Google’s JAX or even their PyTorch/XLA path means rewriting code and retraining teams.

“The biggest Nvidia advantage isn’t the hardware anymore – it’s that their GPUs are available on AWS, Azure, and GCP with zero code changes. With TPUs you’re locked into Google Cloud. That’s terrifying for any company that might want to negotiate prices later.”

– AI startup founder using both platforms

There’s also the multi-cloud reality. Most enterprises have data spread across providers. Moving petabytes between clouds costs real money in egress fees. Nvidia GPUs work the same everywhere. TPUs, for now, only exist in Google data centers.

The Coming Inference Earthquake

Here’s where it gets really interesting though – almost all the ecosystem lock-in matters far less for inference than training.

Training happens rarely. You train a model once (or occasionally fine-tune), then run inference billions of times. That’s where the real money gets spent at scale. And for inference, especially the kind of reasoning workloads we’re moving toward, the software barriers are dropping fast.

Google has been aggressively improving their compiler stack. They’re adding better PyTorch support. They’re making model conversion easier. And most importantly, they’re making older TPU generations dramatically cheaper the moment a new one launches – sometimes practically giving away capacity on previous-gen chips.

The Margin Game Nobody Talks About

This is the part that keeps cloud executives up at night.

When you rent Nvidia GPUs, roughly 75% of what you pay goes straight to Nvidia’s gross margin. The cloud provider gets squeezed into the 20-30% range. That’s utility-level profitability, not the 60-70% margins AWS enjoyed for years.

But when Google runs workloads on its own TPUs? They keep almost everything. Same pricing to the customer, dramatically better margins. Or they can undercut everyone else and still make bank.

Every hyperscaler knows this, which is why Amazon has Trainium/Inferentia and Microsoft has Maia. But Google started a decade earlier. Their software stack is mature. Their compiler team is world-class. And crucially, they’ve designed the chips themselves – not just outsourced to Broadcom.

That in-house expertise matters more than people realize. Each generation gets dramatically better because the same people who wrote Gemini also designed the silicon it runs on. It’s a closed loop nobody else has replicated at this scale.

How Big Could This Actually Get?

Google doesn’t release production numbers, but we can make educated guesses.

They’ve been deploying TPUs at massive scale internally for years. Gemini 3 alone reportedly required clusters that would make most companies bankrupt if they tried to rent equivalent Nvidia capacity. The fact that Google can train frontier models competitively tells you the silicon volume is already enormous.

Add external customers – who are growing rapidly now that Ironwood is available – and you’re looking at one of the largest chip production runs on earth that nobody ever talks about.

And unlike Nvidia, Google doesn’t have to worry about gaming demand collapsing or crypto winters. Their chip has one customer that will never stop growing: Google’s own insatiable need for AI compute.

The Next Ten Years

I’ve been following custom silicon for years, and I honestly can’t remember a strategic advantage this large that was this quietly developed. Apple did something similar with phones, but phones are consumer products everyone sees. Google’s advantage is hidden in data centers most people will never think about.

As we move deeper into an era where inference costs dominate, where every chatbot query and image generation and recommendation click costs real money, the companies that can deliver intelligence cheapest win everything.

Google spent a decade preparing for exactly this moment. Nvidia built an incredible moat with CUDA and developer mindshare. But for the specific problem of running AI at planet scale, Google might have built something even stronger.

The TPU isn’t just a chip. It’s Google’s insurance policy against the entire industry becoming a tax on Nvidia’s hardware margins. And right now, in late 2025, that policy is paying out bigger than anyone outside Mountain View seems to realize.

The AI inference era isn’t coming. It’s already here. And it might be running on chips most people have never heard of.