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 trains all internal models (Gemini, PaLM, etc.) on TPUs.
Compared to GPUs, TPUs offer higher performance per dollar for large training jobs, especially with transformer architectures and LLMs.
TPU Generations
| Generation | Released | TFLOPs | HBM | Strength |
|---|---|---|---|---|
| TPU v2 | 2017 | 180 | 64 GB | Entry-level, affordable |
| TPU v3 | 2018 | 420 | 128 GB | Good price-performance ratio |
| TPU v4 | 2021 | 275 (BF16) | 32 GB | Optimized for LLMs |
| TPU v5e | 2023 | 197 | 16 GB | Cost-optimized |
| TPU v5p | 2023 | 459 | 95 GB | Highest performance |
Core Features
- TPU Pods: Up to thousands of TPUs with high-bandwidth interconnect
- JAX Integration: Native support for JAX and TensorFlow
- Spot/Preemptible: Up to 70% cost savings for fault-tolerant jobs
- Vertex AI Integration: Managed training on TPUs without infrastructure
Typical Use Cases
LLM Training
Training Large Language Models like Llama, Mistral, or custom models. TPU Pods scale to thousands of chips for models with billions of parameters.
Computer Vision
Large vision models (ViT, CLIP) train faster on TPUs. Batch processing of images benefits from TPU architecture.
Scientific Research
Protein folding (AlphaFold), climate models, and other scientific simulations. TPU Research Cloud offers free access for qualified research projects.
TPU vs. GPU on GCP
| Criteria | Cloud TPU | Cloud GPU (A100/H100) |
|---|---|---|
| Frameworks | TensorFlow, JAX | PyTorch, TensorFlow, all |
| Strength | Large training jobs | Flexibility, inference |
| Price/Performance | Better for training | Better for small jobs |
| Availability | Few regions | Many regions |
| Ecosystem | Google-focused | Broader support |
Benefits
- Performance: Optimized for ML workloads, up to 10x cheaper than GPUs
- Scaling: TPU Pods for training the largest models
- Integration: Native support in Vertex AI and GKE
- Spot Pricing: Up to 70% discount for interruptible workloads
Integration with innFactory
As a Google Cloud Partner, innFactory supports you with Cloud TPU: workload analysis, framework migration (PyTorch to JAX), training architecture, and cost optimization.
Typical Use Cases
Technical Specifications
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 matrix operations that dominate neural networks. Google trains internal models like Gemini on TPUs.
When should I use TPU instead of GPU?
TPUs are ideal for large-scale training with TensorFlow or JAX, especially for transformer models and LLMs. GPUs are better for PyTorch (native support), smaller models, or when you need flexible hardware for various workloads.
Which frameworks support Cloud TPUs?
TensorFlow and JAX have native TPU support. PyTorch works via PyTorch/XLA but requires adjustments. For best performance, we recommend JAX for new projects or TensorFlow for existing codebases.
How much do Cloud TPUs cost?
TPU v4 costs from $1.35/hour, TPU v5e from $1.20/hour. Preemptible/Spot TPUs are up to 70% cheaper. For training over weeks, TPU Pods with Committed Use Discounts are most economical.
Are Cloud TPUs available in Europe?
Yes, TPUs are available in europe-west4 (Netherlands). Not all TPU generations are available in all regions. Check the documentation for current availability.
