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Cloud TPU - AI Accelerators for Training and Inference

Cloud TPU (Google Cloud) are specialized AI chips for machine learning, optimized for JAX, PyTorch, and TensorFlow.

Compute
Pricing Model Pay-per-use per hour, tiered by TPU generation; cheaper spot/preemptible pricing and committed-use discounts available
Availability Selected regions, including europe-west4 (Netherlands); availability varies by TPU generation and region
Data Sovereignty EU region europe-west4 available; check availability of individual TPU generations there
Reliability SLA as published by the provider (see official SLA page) SLA

Cloud TPUs are Google’s specialized Tensor Processing Units for machine learning. Optimized for training and inference of large models, from LLMs to computer vision.

What are Cloud TPUs?

TPUs (Tensor Processing Units) are AI accelerators developed by Google, optimized for the matrix multiplications that dominate neural networks. Google uses TPUs, among other things, to train its own models such as the Gemini family.

The TPU lineup is continuously evolving: after the v2 through v5e/v5p generations came TPU v6e (Trillium) and the current Ironwood generation (TPU7x), which Google made generally available around Google Cloud Next 2026. Compared to GPUs, TPUs often offer an attractive price-performance ratio for very large training jobs, especially with transformer architectures and LLMs.

Core Features

  • TPU Pods: Clusters of many TPU chips with high-bandwidth interconnect for very large training runs
  • Current generations: TPU v6e (Trillium) and TPU7x (Ironwood) for training and inference of large models
  • JAX and PyTorch support: Actively developed for JAX and PyTorch/XLA, plus TensorFlow support
  • Spot/Preemptible: Significantly reduced costs for fault-tolerant training jobs
  • Integration with the Gemini Enterprise Agent Platform: Managed training on TPUs without managing your own infrastructure

Typical Use Cases

LLM Training and Fine-Tuning

Training and fine-tuning large language models. TPU Pods scale to many chips for models with billions of parameters.

Computer Vision

Large vision models train on TPUs, and batch processing of images benefits from the TPU architecture.

Scientific Research

Protein folding, climate models, and other compute-intensive scientific simulations use TPU compute for large models and datasets.

TPU vs. GPU on Google Cloud

CriteriaCloud TPUCloud GPU
FrameworksJAX, PyTorch/XLA, TensorFlowPyTorch, TensorFlow, and others, more broadly supported
StrengthVery large training jobs, especially transformer/LLMFlexibility, broad ecosystem, inference
AvailabilitySelected regionsSignificantly more regions
EcosystemGoogle-focusedBroad support across providers

Benefits

  • Optimized for ML: Purpose-built for the matrix operations of neural networks
  • Scaling: TPU Pods for training very large models
  • Integration: Connectivity with the Gemini Enterprise Agent Platform and GKE
  • Spot pricing: Significant cost reduction for interruptible workloads

Integration with innFactory

As a certified Google Cloud partner, innFactory supports you with Cloud TPU: workload analysis, framework selection, training architecture, and cost optimization.

Typical Use Cases

LLM training and fine-tuning
Computer vision training
Reinforcement learning
Scientific simulations

Technical Specifications

Frameworks JAX, PyTorch/XLA, TensorFlow
Generations TPU v2 through v5e/v5p, TPU v6e (Trillium), and TPU7x (Ironwood, GA); older generations may have limited availability
Interconnect High-bandwidth inter-chip interconnect (ICI) for multi-chip and pod configurations

Frequently Asked Questions

What is a Cloud TPU?

TPU (Tensor Processing Unit) is Google's specialized AI chip developed for machine learning workloads. TPUs are optimized for the matrix operations that dominate neural networks. Google trains its own models, such as the Gemini model family, on TPUs.

Which TPU generations are currently available?

In addition to older generations (v2 through v5e/v5p), Google now offers TPU v6e (Trillium) and TPU7x from the Ironwood generation as the current, generally available generation. Not every generation is available in every region; check the official TPU documentation for current details.

When should I use TPU instead of GPU?

TPUs are particularly well suited for large-scale training with JAX or PyTorch/XLA, especially for transformer models and LLMs. GPUs often offer more flexibility across different frameworks, smaller models, or workloads outside ML training.

Which frameworks support Cloud TPUs?

JAX and PyTorch (via PyTorch/XLA) are actively developed by Google for TPUs, and TensorFlow is also supported. The best choice depends on your existing stack and target architecture.

How much do Cloud TPUs cost?

TPUs are billed on a pay-per-use hourly basis, with prices varying by generation. Spot or preemptible TPUs significantly reduce costs for fault-tolerant workloads, and committed-use discounts pay off for long-term training. Current prices are available on the official pricing page.

Are Cloud TPUs available in Europe?

Yes, TPUs are available in europe-west4 (Netherlands), among other regions. Not all TPU generations are available in all regions, so check current availability before starting a project.

Note: All product information on this page has been compiled with care, but is provided without guarantee and may be outdated or incomplete. Cloud services evolve rapidly — features, pricing, SLAs, and availability change frequently. Authoritative and up-to-date information can only be found on the official product page of Google Cloud (official documentation). This page does not represent an offer by Google Cloud.

Google Cloud Partner

innFactory is a certified Google Cloud Partner. We provide expert consulting, implementation, and managed services.

Google Cloud Partner

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