What's the difference between TPU vs. GPU?

Google’s Tensor Processing Unit, known as a TPU, is a chip the company created for cloud-based AI and machine learning tasks in its data centers, but the term also appears in a different form in Google’s Pixel 11 smartphones. In those phones, the TPU is essentially Google’s name for a neural processing unit, or NPU, handling camera and image processing along with local AI tasks. The data center TPU, however, is distinct from both the smartphone version and a traditional NPU or GPU, though they share some similarities. The naming overlap has caused confusion for consumers trying to keep chipset terminology straight.

The data center TPU is an AI accelerator optimized to perform millions of mathematical calculations on the fly in AI models, a job that NPUs and GPUs also handle. NPUs are found in modern smartphones, Macs and PCs, where they handle generative and agentic AI tasks such as photo editing or building travel itineraries. For the Pixel 11 phones, Google replaces the NPU with its on-device TPU, claiming up to 3.5 times faster AI processing while using up to 3.5 times less energy. GPUs, by contrast, are familiar to gamers and 3D modelers, and they continue to run graphically demanding games as well as handle AI training and crypto mining, making them versatile but often expensive.

The main differences between these chips come down to use cases and scale. TPUs are used for cloud-based AI in Google data centers, while NPUs, on-device TPUs and GPUs appear in smartphones and laptops. TPUs have the largest scale by far, with a specialized hardware layout called a systolic array that allows data to move from one unit to the next in a 2D grid of multipliers. That setup eliminates potential bottlenecks because the calculation output becomes the input for the next unit without writing back to memory, unlike GPUs, which juggle data between compute units and high-bandwidth memory.

For large AI companies such as Anthropic and Midjourney, which serve billions of AI requests daily, GPU-based solutions can be costly in terms of bottlenecks, latency and power efficiency. Since a TPU does not cycle back and forth to read and write memory, it is a better fit for training large-scale neural networks and other massive workloads. In smartphones, IoT devices and laptops, NPUs and Google’s on-device TPUs are optimized for low-power tasks like camera effects and real-time translation. A TPU can run an LLM, but only a small and well-defined local one.

Better is relative, as each AI accelerator excels in a specific use case. NPUs and on-device TPUs are better for smaller mainstream AI tasks because their low power consumption suits smartphones, laptops and smartwatches, while still handling real-time translation or background blurring. TPUs are the cost- and energy-efficient solution that runs the AI, machine learning and deep learning show globally, processing millions of calculations while training models at massive scale, purely for big data centers. With their innate flexibility, GPUs can take on both small- and large-scale AI workloads as well as run games, edit video and run simulations, making a beefy GPU the choice for running local LLMs.

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