




Making comparisons with other machine-learning solutions is difficult. Most GPUs have their performance measured in terms of single precision FLOPS, which use 32-bit numbers. The GPUs can typically also operate in double-precision mode (64-bit numbers) and half-precision mode (16-bit numbers). Sometimes, these alternate modes simply halve (for double precision) or double (for half precision) the overall performance, but that’s not universal. Machine learning workloads tend to use these half-precision modes when they can. Google’s first-generation TPUs, however, don’t use floating point at all; they use 8-bit integer approximations to floating point.

Quite how floating point performance maps to these integer workloads isn’t clear, and the ability to use the new TPU for training suggests that Google may be using 16-bit floating point instead. But as a couple of points of comparison: AMD’s forthcoming Vega GPU should offer 13 TFLOPS of single precision, 25 TFLOPS of half-precision performance, and the machine-learning accelerators that Nvidia announced recently—the Volta GPU-based Tesla V100—can offer 15 TFLOPS single precision and 120 TFLOPS for “deep learning” workloads. Nvidia is making similar promises to Google, too, boasting of substantially accelerated training. Microsoft has been using FPGAs for similar workloads, though, again, a performance comparison is tricky; the company has performed demonstrations of more than 1 exa-operations per second (that is, 1018 operations), though it didn’t disclose how many chips that used or the nature of each operation.


Accelerated Machine Learning

Machine learning (ML) has the power to greatly simplify our lives. Improvements in speech recognition and language understanding help all of us interact more naturally with technology. Businesses rely on ML to strengthen network security and reduce fraud. Advances in medical imaging enabled by ML can increase the accuracy of medical diagnoses and expand access to care, ultimately saving lives.

Speed Up Machine Learning Workloads

These revolutionary Cloud TPUs were designed from the ground up to accelerate machine learning workloads. Each Cloud TPU provides up to 180 teraflops of performance, providing the computational power to train and run cutting-edge machine learning models. Cloud TPUs can help you transform your business or create the next research breakthrough.

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Easy On-Ramp to Cloud

Because TensorFlow is open-source, it’s easy to take ML workloads you’re already running in TensorFlow and try them on Cloud TPUs. Use TensorFlow’s high-level APIs to move models across CPUs, GPUs, and TPUs with minimal code changes.

Access Google’s AI Innovation

Access the same accelerators used by Google to develop world-class machine learning products. Cloud TPUs are purpose-built specifically to accelerate state-of-the-art machine learning workloads, including both training and prediction.