Our growing Cloud TPU customer base is already seeing benefits from the scale and performance of Cloud TPU Pods. For example, Recursion Pharmaceuticals can now train in just 15 minutes on Cloud TPU Pods compared to 24 hours on their local GPU cluster.
If cutting-edge deep learning workloads are a core part of your business, please contact a Google Cloud sales representative to request access to Cloud TPU Pods. Google Cloud customers can receive evaluation quota for Cloud TPU Pods in days instead of waiting months to build an on-premise cluster. Discounts are also available for one-year and three-year reservations of Cloud TPU Pod slices, offering businesses an even greater performance-per-dollar advantage.
We’re committed to making our AI platform–which includes the latest GPUs, Cloud TPUs, and advanced AI solutions–the best place to run machine learning workloads. Cloud TPUs will continue to grow in performance, scale, and flexibility, and we will continue to increase the breadth of our supported Cloud TPU workloads (source code available).
To learn more about Cloud TPUs, please visit our Cloud TPU homepage and documentation. You can also try out a Cloud TPU for free, right in your browser, via this interactive Colab that applies a pre-trained Mask R-CNN image segmentation model to an image of your choice. You can find links to many other Cloud TPU Colabs and tutorials at the end of our recent beta announcement.
1. MLPerf v0.6 Training Closed. Retrieved from www.mlperf.org 10 July 2019. MLPerf name and logo are trademarks. See www.mlperf.org for more information.
2. MLPerf entries 0.6-6 vs. 0.6-28, 0.6-6 vs. 0.6-27, 0.6-6 vs. 0.6-30, 0.6-5 vs. 0.6-26, 0.6-3 vs. 0.6-23, respectively.
3. MLPerf entries 0.6-3, 0.6-4, 0.6-5, 0.6-6, respectively, normalized by entry 0.6-1