Cloud TPUs

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Overview

Google Cloud TPUs (Tensor Processing Units) are custom-designed ASICs (application-specific integrated circuits) built to accelerate machine learning workloads. TPUs optimize performance and cost for both AI model training and inference. They are integrated with Google Kubernetes Engine (GKE) and Vertex AI for scalable workload orchestration and a fully-managed AI platform experience. TPUs are designed to efficiently handle large matrix calculations, especially for large language models (LLMs) and recommendation models leveraging SparseCores. Different versions of TPUs are available, including Ironwood, Trillium, v5p, and v5e, each offering varying levels of performance and cost-effectiveness to address different AI workload needs. They provide seamless integration with leading AI frameworks like PyTorch, JAX, and TensorFlow.

Common tasks

AI Model Training AI Model Inference Fine-tuning LLM Acceleration

FAQ

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What is a Cloud TPU?

A Cloud TPU is a custom-designed AI accelerator optimized for training and inference of AI models.

When should I use Cloud TPUs?

Cloud TPUs are optimized for training large and complex deep learning models, especially those with many matrix calculations.

How are Cloud TPUs different from GPUs?

TPUs are ASICs designed specifically for neural networks, while GPUs were originally designed for graphics processing but are also used for AI workloads.

Which AI frameworks are supported by Cloud TPUs?

Cloud TPUs support leading AI frameworks including PyTorch, JAX, and TensorFlow.

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