TPU

TPUs are custom hardware accelerators designed from the ground up for the specific needs of deep learning computations. Unlike CPUs and GPUs, which are general-purpose processors, TPUs are tailored for the matrix multiplications and other linear algebra operations that are fundamental to neural networks. This specialization allows TPUs to achieve significantly higher performance and energy efficiency compared to traditional processors when running machine learning models. Google has developed multiple generations of TPUs, each offering increased computational power and memory capacity. These are used extensively within Google's own infrastructure for training and inference of large language models and other AI applications, and are also available to users through Google Cloud Platform (GCP). The architecture allows for high throughput and low latency, which are critical for both training large models and deploying them for real-time inference. The main design differences from GPUs are higher arithmetic intensity and larger on-chip memory. This leads to less frequent memory accesses and better power efficiency. TPUs have become a cornerstone of modern AI infrastructure, enabling the development and deployment of increasingly complex and powerful AI models.