TPU (Tensor Processing Unit)

Google's custom silicon challenger that could reshape the economics of enterprise AI.

Google's Tensor Processing Units have trained every major Google AI product from Search to Gemini, and are now available to enterprises through Google Cloud with performance-per-dollar claims 2-3x superior to GPU alternatives for specific workloads. As AI infrastructure costs consume an ever-larger share of enterprise IT budgets — projected to reach 15-20% of total technology spend by 2027 — the choice of AI accelerator directly impacts the financial viability of AI strategies. TPUs represent the most credible challenge to NVIDIA's GPU dominance in the AI accelerator market, and for enterprises making multi-year infrastructure commitments, understanding TPU capabilities and limitations is a strategic imperative.

What It Is

A Tensor Processing Unit is a custom-designed semiconductor chip built by Google specifically to accelerate machine learning workloads. Unlike GPUs, which were originally designed for graphics rendering and later repurposed for AI, TPUs were purpose-built from the ground up for the mathematical operations that underpin neural networks. Think of the difference between a Swiss Army knife and a surgical scalpel: both are cutting tools, but one is optimised for versatility while the other is designed for maximum precision and efficiency in a specific task.

TPUs achieve their performance through a radically different architectural approach. Rather than the flexible but general-purpose cores found in GPUs, TPUs use a systolic array architecture — a grid of processing elements that stream data through the chip in a coordinated rhythm, performing matrix multiplications with extraordinary efficiency. This design trades flexibility for raw throughput: TPUs excel at the specific mathematical operations that dominate AI workloads but are not suitable for general computing tasks. For training and running neural networks, this specialisation translates to significantly higher performance per watt and per dollar compared to more general-purpose hardware.

Google has iterated through multiple TPU generations. The original TPU (2016) was designed for inference only. TPU v2 (2017) added training capability with 180 teraflops of performance. TPU v3 (2018) introduced liquid cooling and doubled performance. TPU v4 (2020) delivered 3x improvements over v3 and was used to train Google's largest models. TPU v5 (2023-2024) further extended capabilities with improved performance and efficiency. The latest generation, Trillium, announced at Google I/O 2024, targets the largest frontier model training workloads and competitive inference performance.

Why It Matters for Business

For enterprises running large-scale AI workloads, the economics of TPUs can be compelling. Google Cloud claims that TPU v5 pods offer up to 2-3x better performance per dollar compared to equivalent GPU instances for training large models. For an organisation spending $10 million annually on AI training infrastructure, migrating to TPUs could save $3-5 million per year — savings that can be redirected to hiring AI talent, acquiring training data, or expanding AI-powered products. The performance-per-watt advantage is equally significant for sustainability-conscious organisations, with TPUs consuming an estimated 30-50% less energy per operation than GPUs for the workloads they are designed to handle.

However, TPU adoption comes with important strategic trade-offs. Unlike NVIDIA's GPUs, which run the ubiquitous CUDA software ecosystem, TPUs require Google's proprietary frameworks (primarily JAX and TensorFlow). This creates a meaningful vendor lock-in concern: applications developed for TPUs may require significant re-engineering to run on GPU or other accelerator hardware. For organisations pursuing multi-cloud strategies or those concerned about dependency on a single vendor, this lock-in must be weighed carefully against the cost and performance benefits.

The competitive dynamics between TPUs and NVIDIA GPUs also matter at a macro level. NVIDIA's dominant market position has led to high margins — estimated at 70-80% gross margin on data centre GPUs. Google's TPU programme applies competitive pressure that benefits all buyers, regardless of which platform they choose. As AMD, Intel, and custom silicon programmes from Amazon (Trainium), Microsoft (Maia), and others enter the market, the increasing competition will continue to drive down AI infrastructure costs.

Enterprise Use Cases

Google itself is the largest user of TPUs, employing them across virtually every AI-powered product. Google Search uses TPUs for ranking and recommendation, processing billions of queries daily. YouTube uses TPUs for video recommendation, content moderation, and automatic captioning. Google Translate, Google Photos, and Google Assistant all rely on TPU infrastructure. The scale of Google's internal TPU deployment — estimated at hundreds of thousands of chips across multiple data centres — demonstrates the technology's viability for the most demanding AI workloads in existence.

DeepMind used TPUs exclusively to train its groundbreaking models, including AlphaFold (which predicted the structure of over 200 million proteins and won a Nobel Prize in Chemistry) and Gemini (Google's large language model family). These achievements demonstrate that TPUs are capable of training frontier-class AI models at the highest levels of complexity and scale. For enterprises, this means that the same hardware platform that powers Nobel Prize-winning AI research is available on demand through Google Cloud.

External enterprises have also adopted TPUs. Snapchat uses Google Cloud TPUs for its machine learning features, including augmented reality filters and content recommendations. PayPal has utilised TPUs for fraud detection model training, reporting faster training times and lower costs compared to its previous GPU-based infrastructure. Research institutions including MIT, Stanford, and Oxford use TPUs through Google's TPU Research Cloud programme, advancing scientific AI applications in genomics, climate modelling, and materials science.

Vendor Landscape

TPUs are exclusively available through Google Cloud Platform. The current generation, Trillium, is available as Cloud TPU v5p pods in Google Cloud, with pricing based on usage duration. Google offers TPU access through several models: on-demand pricing (most expensive, no commitment), committed use discounts (1-3 year commitments, 30-50% savings), and pre-emptible TPUs (lowest cost, can be reclaimed by Google). A TPU v5 pod with 8 chips costs approximately $12-20 per hour on-demand, compared to $25-40 per hour for equivalent NVIDIA H100 GPU instances.

The broader AI accelerator market includes NVIDIA (H100, B200 GPUs), AMD (Instinct MI300X), Intel (Gaudi3), Amazon (Trainium2, available on AWS), and Microsoft (Maia, in development). Each platform offers different performance, cost, and ecosystem characteristics. For most enterprises, the choice between TPUs and GPUs should be driven by workload characteristics, existing cloud provider relationships, software ecosystem preferences, and total cost of ownership analysis.

Strategic Considerations

TPUs are best suited for organisations already invested in the Google Cloud ecosystem or those running large-scale training workloads where cost-per-training-run is the primary metric. They are less suitable for organisations heavily invested in NVIDIA's CUDA ecosystem, those requiring multi-cloud portability, or those running diverse workloads that benefit from GPU flexibility. Evaluate TPUs specifically for large model training and high-volume inference — the workloads where their specialisation delivers the greatest advantage. Consider the software migration cost: if your AI team is proficient in PyTorch with CUDA, migrating to JAX on TPUs requires significant retraining and code rewriting. Finally, monitor the evolving competitive landscape: the AI accelerator market is rapidly diversifying, and committing to any single platform for more than 2-3 years carries risk in a market where performance doubles roughly every 18 months.