Generally available: TPU7x is generally available (GA). TPU7x is the first release within the Ironwood family, Google Cloud's seventh generation TPU. TPU7x supports large-scale AI training and inference, providing performance and cost-effectiveness for demanding workloads such as large language (LLMs), mixture of experts (MoEs), and diffusion models. For more information, see the TPU7x (Ironwood) documentation.
]]>Preview: TPU7x is available in Preview. TPU7x is the first release within the Ironwood family, Google Cloud's seventh generation TPU. TPU7x supports large-scale AI training and inference, providing performance and cost-effectiveness for demanding workloads such as large language models (LLMs), mixture of experts (MoEs), and diffusion models. For more information, see the TPU7x (Ironwood) documentation.
]]>Public preview: You can request Cloud TPUs using future reservations in calendar mode. This mode, powered by the Dynamic Workload Scheduler, lets you check TPU availability up to 120 days in advance and request capacity based on your schedule. You can use calendar mode to reserve TPUs for 1 to 90 days. Requesting a short-term reservation with calendar mode is a good fit for training and experimentation workloads that require precise start times and have a defined duration. For more information, see Request a short-term reservation using calendar mode.
Public preview: You can enable reservation sharing for Cloud TPU. This feature lets you share a reservation across multiple projects. You can also share a reservation with Vertex AI for training or serving workloads. For more information, see Share a Cloud TPU reservation.
]]>Flex-start for Cloud TPU, powered by Dynamic Workload Scheduler, is available in Preview. Flex-start is a flexible and cost-effective consumption option for AI workloads. Flex-start enables you to dynamically provision TPUs for up to 7 days using the queued resources API, without long-term reservations. This option is ideal for quick experimentation, small-scale testing, dynamic inference provisioning, and model fine-tuning. For more information about Flex-start for Cloud TPU, see Request Cloud TPUs using Flex-start.
]]>This Release Note announces General Availability of Trillium AKA v6e. Trillium is the 6th generation and latest Cloud TPU. It is fully integrated with our AI Hypercomputer architecture to deliver compelling value to our Google Cloud Platform AI customers.
We used Trillium TPUs to train the new Gemini 2.0, Google's most capable AI model yet, and now enterprises and startups alike can take advantage of the same powerful, efficient, and sustainable infrastructure. Today, Trillium is generally available for Google Cloud customers and this week we will be delivering our first large tranches of Trillium capacity to some of our biggest Google Cloud Platform customers.
Here are some of the key improvements that Trillium delivers over the prior generations, v5e and v5p:
Over 4x improvement in training performance.
Up to 3x increase in inference throughput.
A 67% increase in energy efficiency.
An impressive 4.7x increase in peak compute performance per chip.
Double the High Bandwidth Memory (HBM) capacity.
Double the Interchip Interconnect (ICI) bandwidth.
100,000 Trillium chips per Jupiter network fabric with 13 Petabits/sec of bisection bandwidth, capable of scaling a single distributed training job to hundreds of thousands of accelerators.
Trillium provides up to 2.1x increase in performance per dollar over Cloud TPU v5e and up to 2.5x increase in performance per dollar over Cloud TPU v5p in training dense LLMs like Llama2-70b and Llama3.1-405b.
GKE integration enables seamless AI workload orchestration using Google Compute Engine MIGs including XPK for faster iterative development.
Multislice training with Trillium scales from one to hundreds of thousands of chips across pods using DCN.
Training and serving fungibility enables use of same Cloud TPU quota for both training and inference.
Support for collection scheduling with collection SLOs being defended.
Full-host VM support to enable inference support for larger models (70B+ parameters).
Official Libtpu releases that guarantees stability across all three frameworks (Jax/Pytorch-XLA/Tensorflow).
These enhancements enable Trillium to excel across a wide range of AI workloads, including:
Scaling AI training workloads like LLMs including dense and Mixture of Experts (MoE) models
Inference performance and collection scheduling
Embedding-intensive models acceleration
Delivering training and inference price-performance
You can now request Cloud TPUs as queued resources in the Google Cloud Console. Queuing your request for TPU resources can help alleviate stockout issues. If the resources you request are not immediately available, your request is added to a queue until the request succeeds or you delete it. You can also specify a time range in which you want to fulfill the resource request. For more information, see Manage queued resources.
Creating a Multislice TPU environment is now available in the Google Cloud Console. You can use Multislice to run training jobs using multiple TPU slices within a single Pod or on slices in multiple Pods. You must use a queued resource request to create a Multislice environment. For more information, see Cloud TPU Multislice overview.
]]>Cloud TPU now supports TensorFlow 2.16.1. For more information see the TensorFlow 2.16.1 release notes.
]]>Cloud TPU now supports TensorFlow 2.14.1. For more information see the TensorFlow 2.14.1 release notes.
]]>Cloud TPU now supports TensorFlow 2.15.0, which adds support for PJRT. For more information see the TensorFlow 2.15.0 release notes.
]]>Cloud TPU now supports TensorFlow 2.13.1. For more information see the TensorFlow 2.13.1 release notes.
]]>Cloud TPU now supports TensorFlow 2.14.0. For more information see the TensorFlow 2.14.0 release notes.
]]>You can now create Cloud Tensor Processing Unit (TPU) nodes in Google Kubernetes Engine (GKE) to run AI workloads, from training to inference models. GKE manages your cluster by automating TPU resource provisioning, scaling, scheduling, repairing, and upgrading. GKE provides TPU infrastructure metrics in Cloud Monitoring, TPU logs, and error reports for better visibility and monitoring of TPU node pools in GKE clusters. TPUs are available with GKE Standard clusters. GKE supports TPU v4 in version 1.26.1.gke-1500 and later, and supports TPU v5e in version 1.27.2-gke.1500 and later. To learn more, see TPUs in GKE introduction.
]]>Cloud TPU now supports TensorFlow 2.12.1. For more information see the TensorFlow 2.12.1 release notes.
]]>Cloud TPU now supports TensorFlow 2.13.0. For more information see the TensorFlow 2.13.0 Release Notes.
]]>You can now view historical logs of maintenance events on your TPU in system event audit logs. For additional information see the maintenance events documentation.
]]>Cloud TPU now supports Tensorflow 2.11.1. For more information see the TensorFlow 2.11.1 release notes.
]]>Cloud TPU now supports Tensorflow 2.12.0. For more information see the TensorFlow 2.12 release notes.
]]>Cloud TPUs now support the PyTorch 2.0 release, via PyTorch/XLA integration. On top of the underlying improvements and bug fixes in PyTorch's 2.0 release, this release introduces several features, and PyTorch/XLA specific bug fixes.
xm.rendezvous with XLA collective communication which scales better (#4181)#4520)auto_wrap_policy into XLA FSDP for automatic wrapping (#4318)xla::Shape instead of torch::lazy::Shape (#4111)met.short_metrics_report to display more concise metrics report (#4148)torch.compile) supportCloud TPU now supports TensorFlow patches: 2.8.4, 2.9.3, and 2.10.1. See the TensorFlow release notes for details:
]]>Cloud TPU now supports Tensorflow 2.11.0. For more information see TensorFlow 2.11 release notes.
]]>Cloud TPU now supports Tensorflow 2.10.0. For more information see TensorFlow 2.10 release notes.
]]>Cloud TPU now supports TensorFlow 2.6.5 and TensorFlow 2.7.3.
For more information see TensorFlow 2.6.5 and TensorFlow 2.7.3 release notes.
]]>Cloud TPU now supports Tensorflow 2.8.2 and 2.9.1. For more information see TensorFlow 2.8.2 release notes and TensorFlow 2.9.1 release notes.
]]>Cloud TPU now supports Tensorflow 2.6.3. For more information see TensorFlow 2.6.3 release notes.
]]>Cloud TPU now supports Tensorflow 2.5.3 and 2.7.1. For more information see TensorFlow 2.5.3 release notes and TensorFlow 2.7.1 release notes.
]]>Cloud TPU now supports Tensorflow 2.8.0. For more information, see TensorFlow 2.8.0 Release Notes.
]]>Cloud TPU team just released TF-2.4.4, TF-2.5.2 and TF-2.6.2 on Cloud TPUs. The TensorFlow release notes for these releases are shown below.
]]>Cloud TPU now supports Tensorflow 2.7.0. For more information, see Tensorflow 2.7.0 Release Notes
]]>Cloud TPU team just released TF-2.3.4, TF-2.4.3 and TF-2.5.1 on Cloud TPUs. The TensorFlow release notes for these releases are shown below.
]]>Cloud TPU now supports Tensorflow 2.6.0. For more information, see Tensorflow 2.6.0 Release Notes.
In TF 2.6.0, TensorFlow has introduced a new version of the TF/XLA bridge using the MLIR compiler infrastructure. The MLIR bridge is enabled by default. To explicitly disable it at runtime, add the following code snippet to your model's code:
tf.config.experimental.disable_mlir_bridge()
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