For training and deploying complex models in Artificial Intelligence (AI), the most preferred hardware is the Graphics Processing Units (GPUs). Let's explore why this is the case and evaluate the given options:

• Central Processing Units (CPUs): CPUs are general-purpose processors that are capable of performing a wide range of tasks. However, they are not well-suited for the parallel processing demands of AI model training, which involves handling large datasets and performing billions of calculations. Therefore, CPUs are not the preferred choice for AI model training.
• Graphics Processing Units (GPUs): GPUs are specialized processors that are optimized for parallel processing tasks, such as rendering graphics. This makes them ideally suited for training AI models, which require handling and processing large amounts of data concurrently. The ability of GPUs to perform many operations simultaneously significantly accelerates the training process, making them the preferred choice for AI applications.
• Read Only Memory (ROM): ROM is a type of non-volatile storage used primarily for storing firmware. It is not involved in processing or training AI models.
• Solid State Drives (SSDs): SSDs are storage devices used for quickly reading and writing data. While they are important for data storage and retrieval in AI applications, they do not directly perform the computational tasks needed for model training.

Given these considerations, Graphics Processing Units (GPUs) are the optimal choice for training and deploying complex AI models due to their high parallel processing capabilities, making them the correct answer.