GPU (Graphics Processing Unit) is a specialized processor that is optimized for performing the complex mathematical calculations required for graphics rendering. However, it’s also well-suited for other types of workloads that involve parallelizable computations, such as deep learning and other machine learning tasks.
In AI, GPUs are commonly used for training deep neural networks, which involve a large number of matrix multiplications and other mathematical operations. These operations can be parallelized, which means that they can be split up and performed simultaneously by multiple cores on a GPU. This allows for much faster training times compared to using a CPU alone.
Additionally, GPUs are also often used for inference, which is the process of using a trained model to make predictions on new data. During inference, a GPU’s ability to perform parallelizable calculations can also be utilized to accelerate the inference process.
In summary, a GPU is a specialized processor that is well-suited for artificial intelligence workloads that involve parallelizable computations, such as deep learning and other machine learning tasks. It can be used to speed up both training and inference processes, and it is commonly used alongside a CPU in AI systems.
