TPU v5e
This document describes the architecture and supported configurations of Cloud TPU v5e.
TPU v5e supports single and multi-host training and single-host inference. Multi-host inference is supported using Sax. For more information, see Large Language Model Serving.
System architecture
Each v5e chip contains one TensorCore. Each TensorCore has four matrix-multiply units (MXUs), a vector unit, and a scalar unit.
The following diagram illustrates a TPU v5e chip.
The following table shows the key chip specifications and their values for v5e.
| Key chip specifications | v5e values |
| Peak compute per chip (bf16) | 197 TFLOPs |
| HBM2 capacity and bandwidth | 16 GB, 819 GBps |
| Interchip Interconnect BW | 1600 Gbps |
The following table shows Pod specifications and their values for v5e.
| Key Pod specifications | v5e values |
| TPU Pod size | 256 chips |
| Interconnect topology | 2D Torus |
| Peak compute per Pod | 100 PetaOps(Int8) |
| All-reduce bandwidth per Pod | 51.2 TB/s |
| Bisection bandwidth per Pod | 1.6 TB/s |
| Data center network bandwidth per Pod | 6.4 Tbps |
Configurations
Cloud TPU v5e is a combined training and inference (serving) product. To
differentiate between a training and an inference environment, use the
AcceleratorType or AcceleratorConfig flags with the TPU API or the
--machine-type flag when creating a GKE node
pool.
Training jobs are optimized for throughput and availability, while serving jobs are optimized for latency. A training job on TPUs provisioned for serving could have lower availability and similarly, a serving job executed on TPUs provisioned for training could have higher latency.
You use AcceleratorType to specify the number of TensorCores you want to use.
You specify the AcceleratorType when creating a TPU using the
gcloud CLI or the Google Cloud console. The value you
specify for AcceleratorType is a string with the format:
v$VERSION_NUMBER-$CHIP_COUNT.
You can also use AcceleratorConfig to specify the number of TensorCores you
want to use. However, because there are no custom 2D topology variants for TPU
v5e, there is no difference between using AcceleratorConfig and
AcceleratorType.
To configure a TPU v5e using AcceleratorConfig, use the --version and the
--topology flags. Set --version to the TPU version you want to use and
--topology to the physical arrangement of the TPU chips in the slice. The
value you specify for AcceleratorConfig is a string with the format AxB,
where A and B are the chip counts in each direction.
The following 2D slice shapes are supported for v5e:
| Topology | Number of TPU chips | Number of Hosts |
| 1x1 | 1 | 1/8 |
| 2x2 | 4 | 1/2 |
| 2x4 | 8 | 1 |
| 4x4 | 16 | 2 |
| 4x8 | 32 | 4 |
| 8x8 | 64 | 8 |
| 8x16 | 128 | 16 |
| 16x16 | 256 | 32 |
Each TPU VM in a v5e TPU slice contains 1, 4 or 8 chips. In 4-chip and smaller slices, all TPU chips share the same Non Uniform Memory Access (NUMA) node.
For 8-chip v5e TPU VMs, CPU-TPU communication will be more efficient within NUMA
partitions. For example, in the following figure, CPU0-Chip0 communication will
be faster than CPU0-Chip4 communication.
Cloud TPU v5e types for serving
Single-host serving is supported for up to 8 v5e chips. The following configurations are supported: 1x1, 2x2 and 2x4 slices. Each slice has 1, 4 and 8 chips respectively.
To provision TPUs for a serving job, use one of the following accelerator types in your CLI or API TPU creation request:
| AcceleratorType (TPU API) | Machine type (GKE API) |
|---|---|
v5litepod-1 |
ct5lp-hightpu-1t |
v5litepod-4 |
ct5lp-hightpu-4t |
v5litepod-8 |
ct5lp-hightpu-8t |
Serving on more than 8 v5e chips, also called multi-host serving, is supported using Sax. For more information, see Large Language Model Serving.
Cloud TPU v5e types for training
Training is supported for up to 256 chips.
To provision TPUs for a v5e training job, use one of the following accelerator types in your CLI or API TPU creation request:
| AcceleratorType (TPU API) | Machine type (GKE API) | Topology |
|---|---|---|
v5litepod-16 |
ct5lp-hightpu-4t |
4x4 |
v5litepod-32 |
ct5lp-hightpu-4t |
4x8 |
v5litepod-64 |
ct5lp-hightpu-4t |
8x8 |
v5litepod-128 |
ct5lp-hightpu-4t |
8x16 |
v5litepod-256 |
ct5lp-hightpu-4t |
16x16 |
v5e TPU VM type comparison:
| VM Type | n2d-48-24-v5lite-tpu | n2d-192-112-v5lite-tpu | n2d-384-224-v5lite-tpu |
| # of v5e chips | 1 | 4 | 8 |
| # of vCPUs | 24 | 112 | 224 |
| RAM (GB) | 48 | 192 | 384 |
| # of NUMA Nodes | 1 | 1 | 2 |
| Applies to | v5litepod-1 | v5litepod-4 | v5litepod-8 |
| Disruption | High | Medium | Low |
To make space for workloads that require more chips, schedulers may preempt VMs with fewer chips. So 8-chip VMs are likely to preempt 1 and 4-chip VMs.