TPU regions and zones

Overview

The main differences between TPU types are price, performance, memory capacity, and zonal availability.

Google Cloud Platform uses regions, subdivided into zones, to define the geographic location of physical compute resources. For example, the us-central1 region denotes a region near the geographic center of the United States. When you create a TPU node, you specify the zone in which you want to create it. See the Compute Engine Global, regional, and zonal resources document for more information about regional and zonal resources.

You can create your TPU configuration in the zones shown in the following table.

US

TPU type (v2) TPU v2 cores Total TPU memory Region/Zone
v2-8 8 64 GiB us-central1-b
us-central1-c
us-central1-f
v2-32 32 256 GiB us-central1-a
v2-128 128 1 TiB us-central1-a
v2-256 256 2 TiB us-central1-a
v2-512 512 4 TiB us-central1-a
TPU type (v3) TPU v3 cores Total TPU memory Available zones
v3-8 8 128 GiB us-central1-a
us-central1-b
us-central1-f

Europe

TPU type (v2) TPU v2 cores Total TPU memory Region/Zone
v2-8 8 64 GiB europe-west4-a
v2-32 32 256 GiB europe-west4-a
v2-128 128 1 TiB europe-west4-a
v2-256 256 2 TiB europe-west4-a
v2-512 512 4 TiB europe-west4-a
TPU type (v3) TPU v3 cores Total TPU memory Available zones
v3-8 8 128 GiB europe-west4-a
v3-32 32 512 GiB europe-west4-a
v3-64 64 1 TiB europe-west4-a
v3-128 128 2 TiB europe-west4-a
v3-256 256 4 TiB europe-west4-a
v3-512 512 8 TiB europe-west4-a
v3-1024 1024 16 TiB europe-west4-a
v3-2048 2048 32 TiB europe-west4-a

Asia Pacific

TPU type (v2) TPU v2 cores Total TPU memory Region/Zone
v2-8 8 64 GiB asia-east1-c

TPU types with higher numbers of chips or cores are available only in limited quantities. TPU types with lower chip or core counts are more likely to be available.

Calculating price and performance tradeoffs

To decide which TPU type you want to use, you can do experiments using a Cloud TPU tutorial to train a model that is similar to your application.

Run the tutorial for 5 - 10% of the number of steps you will use to run the full training on a v2-8, or a v3-8 TPU type. The result tells you how long it takes to run that number of steps for that model on each TPU type.

Because performance on TPU types scales linearly, if you know how long it takes to run a task on a v2-8 or v3-8 TPU type, you can estimate how much you can reduce task time by running your model on a larger TPU type with more chips or cores.

For example, if a v2-8 TPU type takes 60 minutes to 10,000 steps, a v2-32 node should take approximately 15 minutes to perform the same task.

When you know the approximate training time for your model on a few different TPU types, you can weigh the VM/TPU cost against training time to help you decide your best price/performance tradeoff.

To determine the difference in cost between the different TPU types for Cloud TPU and the associated Compute Engine VM, see the TPU pricing page.

Specifying the TPU type

Regardless of which framework you are using, TensorFlow, PyTorch, or JAX, you specify a v2 or v3 TPU type with the accelerator-type parameter when you launch a TPU. The TPU type command depends on whether you are using TPU VMs or TPU Nodes. Example commands are shown in Managing TPUs.

What's next

  • To see pricing for TPUs in each region, see the Pricing page.
  • Learn more about TPU architecture in the System Architecture page.
  • See When to use TPUs to learn about the types of models that are well suited to Cloud TPU.