Google Cloud unveils the world’s largest publicly available ML hub with Cloud TPU v4, 90% carbon-free energy. Learn more.

Cloud TPU

Train and run machine learning models faster than ever before.

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Empowering businesses with Google Cloud AI

Machine learning has produced business and research breakthroughs ranging from network security to medical diagnoses. We built the Tensor Processing Unit (TPU) in order to make it possible for anyone to achieve similar breakthroughs. Cloud TPU is the custom-designed machine learning ASIC that powers Google products like Translate, Photos, Search, Assistant, and Gmail. Here’s how you can put the TPU and machine learning to work accelerating your company’s success, especially at scale.
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Built for AI on Google Cloud

Cloud TPU is designed to run cutting-edge machine learning models with AI services on Google Cloud. And its custom high-speed network offers over 100 petaflops of performance in a single pod—enough computational power to transform your business or create the next research breakthrough.

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Iterate faster on your ML solutions

Training machine learning models is like compiling code: you need to update often, and you want to do so as efficiently as possible. ML models need to be trained over and over as apps are built, deployed, and refined. Cloud TPU’s robust performance and low cost make it ideal for machine learning teams looking to iterate quickly and frequently on their solutions.

Proven, state-of-the-art models

You can build your own machine learning-powered solutions for many real-world use cases. Just bring your data, download a Google-optimized reference model, and start training.

Customers and partners

“At Cohere, we build cutting-edge natural language processing (NLP) services, including APIs for language generation, classification, and search. These tools are built on top of a set of language models that Cohere trains from scratch on Cloud TPUs using JAX. We saw a 70% improvement in training time for our largest model when moving from Cloud TPU v3 Pods to Cloud TPU v4 Pods, allowing faster iterations for our researchers and higher quality results for our customers. The exceptionally low carbon footprint of Cloud TPU v4 Pods was another key factor for us.”

Aidan Gomez, CEO and co-founder

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“LG AI Research, as a strategic research partner, participated in testing TPU v4 before commercialization, Google’s latest machine learning supercomputer, to train LG EXAONE, a super-giant AI that has 300 billion parameters scale. Equipped with multimodal capabilities, LG EXAONE had been training with TPU v4 and a huge amount of data, more than 600 billion text corpus and 250 million images, aiming to surpass human experts in terms of communication, productivity, creativity, and many more aspects. Not only did the performance of TPU v4 outperform other best-in-class computing architectures, but also the customer-oriented support was beyond our expectations. We were very excited to collaborate with Google and expect to solidify the strategic partnership to achieve our ultimate vision, advancing AI for a better life.”

 Kyunghoon Bae, PhD, Chief of LG AI research

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“Early access to TPU v4 has enabled us to achieve breakthroughs in conversational AI programming with our CodeGen project, a 16-billion parameter auto-regressive language model that turns simple English prompts into executable code. The large size of this model is motivated by the empirical observation that scaling the number of model parameters proportional to the number of training samples appears to strictly improve the performance of the model. The phenomenon is known as the scaling law. TPU v4 is an outstanding platform for this kind of scale-out ML training, providing significant performance advantages over other comparable AI hardware alternatives.”

Erik Nijkamp, Research scientist, Salesforce

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"Based on our recent survey of 2000 IT decision makers, we found that inadequate infrastructure capabilities are often the underlying cause of AI projects failing. To address the growing importance for purpose-built AI infrastructure for enterprises, Google launched its new machine learning cluster in Oklahoma with nine exaflops of aggregated compute. We believe that this is the largest publicly available ML hub with 90% of the operation reported to be powered by carbon free energy. This demonstrates Google's ongoing commitment to innovating in AI infrastructure with sustainability in mind".

Matt Eastwood, IDC Senior Vice President, Enterprise infrastructure, cloud, telecom, security, developers, channels, and enabling tech 

Features

Model Library

Get started immediately by leveraging our growing library of optimized models for Cloud TPU. These provide optimized performance, accuracy, and quality in image classification, object detection, language modeling, speech recognition, and more.

Connect Cloud TPUs to custom machine types

You can connect to Cloud TPUs from custom Deep Learning VM Image types, which can help you optimally balance processor speeds, memory, and high-performance storage resources for your workloads.

Fully integrated with Google Cloud

At their core, Cloud TPUs and Google Cloud’s data and analytics services are fully integrated with other Google Cloud offerings, like Google Kubernetes Engine (GKE). So when you run machine learning workloads on Cloud TPUs, you benefit from Google Cloud’s industry-leading storagenetworking, and data analytics technologies.

Preemptible Cloud TPU

You can save money by using preemptible Cloud TPUs for fault-tolerant machine learning workloads, such as long training runs with checkpointing or batch prediction on large datasets. Preemptible Cloud TPUs are 70% cheaper than on-demand instances, making everything from your first experiments to large-scale hyperparameter searches more affordable than ever.

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