While GPUs provide significant acceleration for the parallel computations common in AI, they are not the only specialized hardware available. Google developed its own custom Application-Specific Integrated Circuits (ASICs) called Tensor Processing Units (TPUs) specifically designed to accelerate machine learning workloads built with TensorFlow, although they can be used with other frameworks too.

Think of TPUs as processors whose architecture is fundamentally optimized for the types of large matrix multiplications and other operations, known as tensor operations, that are at the heart of training and running neural networks, including LLMs. While GPUs are excellent general-purpose parallel processors adapted for these tasks, TPUs were designed from the ground up with this primary goal in mind.

The core idea behind TPUs is to perform a massive number of these tensor operations simultaneously and with high power efficiency. This specialization can lead to significant performance improvements, particularly for large-scale model training and inference tasks, compared to using only CPUs or even some GPU configurations, especially when dealing with the specific types of calculations they excel at.

However, TPUs are not as widely accessible as GPUs. They are primarily available through Google Cloud Platform (GCP) and are used extensively within Google for services like Search, Translate, and Photos. For developers and researchers outside the Google ecosystem, GPUs often remain the more common and accessible choice for accelerating AI tasks.

In summary, TPUs represent another important piece of the AI hardware picture. They are highly specialized processors designed by Google to excel at the mathematical operations fundamental to deep learning, offering an alternative to GPUs, particularly within the Google Cloud environment and for large-scale machine learning tasks. Understanding their existence helps complete the overview of hardware commonly used in AI today, alongside the more universally accessible CPUs, RAM, and GPUs.