Cloud TPU v4: Fast, flexible, and easy-to-use ML accelerators
summary
Cloud TPU v4 doubles per-chip speed versus v3, packs 4,096 v4 chips into a pod with 10x interconnect bandwidth per chip and delivers more than one exaflop of peak compute. MLPerf runs use >3,400 chips to train ResNet‑50 in 14 seconds, beating earlier GPU times (29 hours in 2015). TPU VM gives root access for local storage and input pipelines, and the stack supports TensorFlow, JAX, and PyTorch with distribution strategies and a Keras codelab for scaling from single-device tests to full pod slices.
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chapters (7)
- 0:00Intro
- 0:24How can we train ML models faster?
- 1:04Cloud TPU v3
- 2:54Run multiple frameworks on Cloud TPUs
- 3:09Typical Cloud TPU development workflow
- 4:33Scale batch size (and LR) with TPU cores
- 5:01An all-new Cloud TPU user experience
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