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TPU? GPU? What's the difference between these two chips used for AI?
10 Feb 2026
Chapter 1: What is the difference between TPUs and GPUs?
Do you know your TPUs from your GPUs? From American Public Media, this is Marketplace Tech. I'm Megan McCarty Carino. GPUs, or graphics processing units, have become the most important commodity in the AI boom and made Nvidia a multi-trillion dollar company. But they could have competition from a different three-letter chip, the TPU, or Tensor Processing Unit.
Chapter 2: How are TPUs developed specifically for AI workloads?
These are developed by Google specifically for AI workloads. Anthropic, OpenAI, and Meta have reportedly made deals for Google TPUs. For more on what this means, we've got Christopher Miller, historian at Tufts and author of the book Chip War.
Google was realizing that because it owned YouTube and Google Search and many other applications, had to do many of the same types of calculations. And that was why Google started devising its own in-house chip design arm. And that lets them be faster than the more general purpose AI chips that NVIDIA sells, or faster at least for the specific use cases that Google needs them for.
Yeah, I mean, what kinds of advantages do TPUs have over GPUs for these specific use cases?
Well, it's really all about speed and power consumption. The more tailored the chip to a specific use, the more it can be efficient than a general-purpose chip.
But there's another side of that trade-off, which is that the more specific the chip, the fewer the use cases it can be used for, which is why for most of the AI ecosystem, NVIDIA's more general-purpose GPU chips are still the most commonly used.
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Chapter 3: What advantages do TPUs have over GPUs in AI applications?
And whenever we talk about AI processing, we often sort of break it down into training versus inference. Training is kind of the most processing heavy part where, you know, you're just you're processing these vast amounts of data for the machine learning. And then inference is kind of slightly less processing heavy. Say you're using a chatbot and ask it a question.
Are these primarily for one or the other or both of those parts of the AI process?
Chapter 4: How do TPUs and GPUs contribute to AI training and inference?
Well, for Google's TPU and NVIDIA's GPU, they're used for both. There are other types of specialized AI chips that focus on one or the other, and particularly on inference, because Google's and NVIDIA's ecosystems are especially capable of training. And I think we're going to see over time more and more specialization.
Just as we use more AI, it will become economically viable to have more specialized hardware for certain types of use cases.
Right, and perhaps one of those specialized hardware might be neural processing units. These have been around for a while, as many AI applications have been around for a while. But as this particular type of processing-heavy AI becomes more common on devices, are neural processing units on our devices becoming a bigger focus?
They certainly are. We already see in the newest PCs and phones specific types of chips that are designed to accelerate the types of AI that are already being deployed on devices like this.
And I think as we use more AI in more types of devices, in cars, for example, in robots and industrial equipment, there will in some cases be specialized chips to accelerate the specific types of AI workloads that are used in those different domains.
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Chapter 5: What role do neural processing units play in AI hardware?
We'll be right back. You're listening to Marketplace Tech. I'm Megan McCarty Carino. We're back with Christopher Miller, author of the book Chip War, the fight for the world's most critical technology. How big of a threat do Google's TPUs appear to pose to NVIDIA GPUs?
Well, we're going to find out over the next couple of years. Until very recently, Google didn't sell its chips to anyone else. It developed its chips for its own purposes. But now that looks like it's beginning to change. But of course, NVIDIA's got an extraordinary market position right now. And so it'll be quite the competition to watch the two of them compete for AI market share.
One of the sort of themes of your writing on the chips industry in your book was just how kind of concentrated the industry was. And I mean, it seems like in the AI era, you know, that concentration has been really important. It's driven NVIDIA to the heights that it's been, you know, attained. Do you see that kind of concentration in the industry changing anytime soon?
Chapter 6: How does industry concentration affect the competition between TPU and GPU?
There are a number of dynamics that encourage that concentration. One is the need for extraordinary volumes of R&D to continue improving chips at the rate that companies like NVIDIA or Google are able to do. Look at those two companies' R&D budgets. They're at a scale that very few startups can even dream of. That's one dynamic that encourages scale.
The second is that the chips have to interact with a software ecosystem around them. NVIDIA has spent the last decade building out its software ecosystem, and any new player finds itself far behind when it comes to the depth of the ecosystem around NVIDIA.
That was Christopher Miller, author of the book Chip War. Jesus Alvarado produced this episode. I'm Megan McCarty Carino, and that's Marketplace Tech.
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