What’s the difference between TPU vs. GPU?
Google’s Tensor Processing Unit (TPU) is a specialised AI accelerator originally built for large-scale cloud computing in the company’s data centres, while GPUs are more general-purpose processors used for graphics, AI training and other demanding tasks. The distinction matters because Google also calls the AI hardware in its Pixel 11 phones a TPU, although in that setting it effectively serves the role normally associated with a neural processing unit (NPU).
Data-centre TPUs are designed for massive AI and machine-learning workloads, such as training large language models, using a systolic-array layout that passes calculations directly between units rather than repeatedly accessing memory. This can reduce bottlenecks, latency and energy use compared with GPUs for enormous workloads; meanwhile, NPUs and phone-based TPUs handle local tasks such as image processing and generative AI. Google says the Pixel 11’s Tensor G6 offers 50% more TPU computing power and up to 3.5 times faster AI processing while using up to 3.5 times less energy.
- TPUs specialise in efficient large-scale AI computation.
- Pixel TPUs effectively function as on-device NPUs.
- GPUs remain flexible processors for graphics and AI.
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A GPU is a processor that can handle many calculations at once. It was first widely used to create graphics, but its flexibility has made it useful for artificial intelligence and other demanding work. Google’s TPU, or Tensor Processing Unit, is designed specifically to speed up AI calculations.
Large TPUs in data centres help run or train AI systems at scale, while smaller AI processors in phones handle tasks on the device, such as image processing. Google uses the name TPU for the Pixel’s phone-based AI hardware; other makers often call a similar component an NPU. The distinction matters because the same terms can refer to hardware built for very different settings and workloads.