What is TPU?
A TPU (Tensor Processing Unit) is a custom chip designed by Google to accelerate machine learning workloads, especially matrix multiplications used in neural networks.
TPUs are application-specific integrated circuits (ASICs) optimized for tensor operations rather than general-purpose computing. They use a systolic array architecture that efficiently pipelines large numbers of multiply-accumulate operations.
Unlike CPUs or GPUs, TPUs are tailored for the dataflow patterns common in deep learning frameworks such as TensorFlow and JAX, delivering higher throughput per watt for both training and inference.
They are available as cloud accelerators (e.g., Google Cloud TPU pods) and are connected via high-bandwidth interconnects to scale across many chips for very large models.
Example
A researcher training a large language model on Google Cloud can attach a TPU v4 pod instead of hundreds of GPUs, often completing training runs in fewer days while consuming less power.
Why it matters
TPUs make large-scale AI training more cost-effective and energy-efficient, enabling organizations to iterate faster on bigger models that would otherwise be impractical.
Frequently asked questions
Tensor Processing Unit, a Google-designed chip specialized for tensor math in AI.
Related terms
A GPU (Graphics Processing Unit) is a specialized processor with thousands of small cores optimized for parallel computations, widely used to speed up AI and machine learning workloads.
A neural network, or artificial neural network (ANN), is a computational model inspired by the human brain that learns to recognize patterns in data by passing information through layers of interconnected artificial neurons.
Inference is the stage where a trained machine learning model is used to generate predictions or outputs on new, unseen data. In infrastructure contexts, it focuses on efficiently deploying and serving models in production.
An API (Application Programming Interface) is a standardized set of rules that lets software applications request services or data from each other. In AI infrastructure, it typically means exposing machine learning models as callable endpoints for inference or training.
CUDA is NVIDIA's platform and programming model that lets developers run general-purpose computations on NVIDIA GPUs instead of just CPUs.
Knowledge distillation is a technique that transfers knowledge from a large, complex 'teacher' model to a smaller 'student' model so the student can achieve similar performance with far less compute and memory.