TPUs, or Tensor Processing Units, are custom-designed AI accelerators developed by Google to handle machine learning workloads more efficiently than general-purpose chips. Unlike Nvidia’s GPUs, which were originally built for gaming and later adapted for AI, TPUs were created from the ground up for artificial intelligence tasks. This specialization allows them to excel in both training large AI models and inference—the process of using a trained model to generate responses or make predictions. Google first introduced TPUs over a decade ago, driven by the need for faster, more efficient computation as its AI ambitions grew. The chips are built around a unique architecture called a systolic array, which enables a continuous flow of data through the processor, reducing the need to repeatedly access memory. This design boosts performance and energy efficiency, particularly for neural network computations. Over time, Google has released several generations of TPUs. The latest, the Ironwood TPU, launched in November and is said to be more than four times faster than its predecessor in both training and inference tasks. Google is now making Ironwood available more widely, including through its cloud platform, Google Cloud, to external customers. One of the key advantages of TPUs is their scalability. Google can link thousands of them into a single "pod," creating massive parallel processing power. This makes them especially cost-effective for large-scale AI operations, particularly as companies invest more in inference, where models are used in real-time applications. Despite these strengths, widespread adoption of TPUs has been limited. A major barrier is software compatibility. Nvidia’s CUDA platform has become the de facto standard for AI development, offering broad support across tools and frameworks. In contrast, Google’s TPU ecosystem has historically been tied to its own Tensorflow framework, which has seen declining use compared to PyTorch—a tool developed by Meta and now widely adopted across the industry. To address this, Google is increasing its support for PyTorch, aiming to make TPUs more accessible to developers and companies already using the popular framework. This shift could help expand TPU adoption beyond Google’s internal use. Google remains its own largest user of TPUs, leveraging them to power products like Search, Maps, and its Gemini AI models. However, the company has also begun leasing TPUs to external clients. Apple used them to train its on-device AI models, and Anthropic recently announced a major deal to use up to 1 million TPUs. Broadcom, which manufactures the chips for Google, reported $21 billion in orders from Anthropic alone. Meta is also testing TPUs, signaling growing interest from other AI leaders. While Google has been cautious about expanding TPU sales, analysts believe the market potential is massive. Morgan Stanley estimates that Google could sell 5 million TPUs by 2027 and 7 million by 2028, potentially generating tens of billions in revenue. While TPUs are not yet a direct threat to Nvidia’s dominance, they represent a growing challenge. Other tech giants like Amazon are also developing custom AI chips, such as Trainium3, which promises to cut training costs in half compared to GPUs. This trend toward specialized hardware could lead to a more diverse AI chip market, reducing reliance on any single provider. Experts say that while Nvidia still leads the market, the rise of TPUs and similar chips may shift pricing power and encourage companies to use multiple chip types. Google’s TPU business could become a major growth engine, but it will depend on broader software support and customer trust. For now, TPUs are a powerful tool in Google’s AI arsenal—and a potential game-changer in the wider AI hardware landscape.