Use cases and case studies
TPUs are ideal for applications involving massive datasets and complex neural networks. This page walks the workloads where Cloud TPUs earn their place - who runs them and why.
Training and fine-tuning foundation models (Generative AI)
Large AI research labs, tech companies (including Google for its Gemini models), and startups building cutting-edge AI lease large clusters of TPUs - known as TPU Pods - to train colossal foundation models like Large Language Models (LLMs) or generative image models from scratch.
The specialized architecture lets billions or even trillions of parameters be updated efficiently across hundreds or thousands of chips in parallel, significantly cutting the time and cost of these computationally intensive tasks. The same clusters are also used to fine-tune pre-trained models on smaller, task-specific datasets - adapting a general model for a particular use case.
Accelerating recommendation systems
E-commerce platforms, streaming services, and social media companies run recommendation engines that analyze vast amounts of user-interaction data to predict preferences. This work centers on high-dimensional, sparse matrices (where most values are zero).
TPUs are equipped with special units called SparseCores that are particularly efficient at the sparse-matrix operations common in embedding-based recommendation models - letting these platforms process billions of interactions and serve real-time, personalized recommendations.
Scientific research and complex data analysis
Fields like drug discovery, protein-folding modeling, and climate simulation involve complex numerical computations that can be framed as large-scale matrix operations. Simulating molecular interactions or analyzing large genomic datasets demands immense parallel processing power.
With their focus on high-throughput matrix multiplication, TPUs let researchers run these simulations and analyses much faster - accelerating discovery and pushing the boundaries of scientific understanding.
Key takeaways: scaling architectures
From massive model training to efficient inference, TPUs are built for today's cutting-edge AI. The table below outlines the key scaling architectures for Google's ML accelerators - their parallelism, target use cases, and value proposition.
| Version | Scaling architecture | Use cases | Value proposition |
|---|---|---|---|
| v5e | 2D parallelism within 16x16 (256 chips/pod) Pods; Multislice scaling to 100s of v5e pods (50K with benchmarking) at 6.4 Tbps per pod | General purpose and large-model ML inference, fine-tuning and training | Scaling enabled through Cloud TPU's Multislice technology; highest performance/TCO for LLM inference |
| v5p | 3D parallelism, 8,960 chips per pod; Multislice scaling to 345.6 Tbps DCN | General purpose ML training (vision, media generation, recommenders, communication-heavy LLMs) | Effortless scaling; highest flop utilization; highest training performance; lowest latency serving |
| Trillium | 2D parallelism within 16x16 (256 chips/pod) Pods; Multislice scaling in cluster to 497 Trillium pods (127K chips) at 25.6 Tbps per pod | LLM training and inference; SparseCore support for embeddings (and flexibility in offload) | Huge scalability through cluster design; highest performance/TCO LLM training |
The three architectures overlap but tilt differently - v5p for the highest training performance and lowest-latency serving, v5e for the best performance/TCO on LLM inference, and Trillium for the best performance/TCO on LLM training plus SparseCore embedding support. Understanding these use cases helps identify how Cloud TPUs best serve a specific AI development and deployment need.