A Beginner’s guide to AI Processors: CPUs, GPUs, TPUs and LPUs

Examples : Intel Core processors and Apple M1/M2 chips

Best for: General-purpose computing, smaller datasets, or tasks requiring complex logic where data processing needs are sequential.

Application: Running data preprocessing or light machine learning models like linear/logistic regression on a dataset with less number of data poin

Examples: Nvidia Tesla and Nvidia Drive PX

Best for: Large-scale machine learning tasks that requires handling of massive datasets with millions of data points or running complex deep learning models.

Application: Training a convolutional neural network on a dataset with millions of images. A task that could take days on a CPU might only take hours on a GPU due to its parallel processing capabilities.

Examples: Apple neural engine and Intel’s Gaudi AI processor

Best for: TPUs are built to enhance the speed and efficiency of TensorFlow applications, making them particularly suitable for tasks that can leverage this specific framework.

Application: TPUs are commonly used in advanced Google services like Google Search and Google Photos, where rapid data processing and model improvements are continously required.

Groq, a new player, in the AI hardware industry, is pioneering the development of LPUs

Best for: LPUs are suitable for applications that require processing complex language data efficiently.

Example: LPUs power advanced chatbots, enabling them to understand and respond to human queries quickly and accurately, improving user interactions in customer service settings.

CPUs are suitable for general-purpose computing and less complex AI tasks.

GPUs are the go-to for large-scale data processing and deep learning

TPUs offer the best performance for TensorFlow-based applications.

LPUs excel in handling optical data for imaging and real-time analysis.