Scalability of Generative AI Models: Challenges and Opportunities in Large-Scale Data Generation and Training

Abstract

Generative models of artificial intelligence have revolutionized many sectors of society, allowing machines to create content similar to humans in fields ranging from text, images and music to code. Many creative ones are possible based on deep learning models such as GANs, VAEs, and Transformer category models. The use of generative AI by most businesses has positively impacted creative arts, content creation, healthcare, and software solutions by helping realize revolutions in terms of output without compromising on quality. Though the trends described above enhance these models, scaling those models to huge growth in terms of the required computational resources, large data sets and RT instances remains a major technical as well as logistical problem. However, for generative AI to continue improving with the highest performance and within the simplest and cheapest means available, the demand for high-performing hardware, efficient training approaches, and intelligent data pathways is imperative.

A need to scale generative AI models means that even more challenges will arise with regard to computational limitations as well as data input, as well as other concerns, such as integration with current technological structures. Due to the huge volume of available data for training, training data storage and preprocessing, along with methods that can increase the amount of training data, are challenging tasks for preserving the high performance and effectiveness of machine learning models. Also, as the dependence on generative AI in content generation rises, issues of ethics like bias, fake news, and legal issues come into the frame. Ethical approaches and decentralization of decision-making to avoid risks are always important when it comes to deployment. Moreover, incorporating efficient AI models in applications, clouds, and edge computing systems requires AI APIs, distributed computing frameworks, and effective inference techniques. This paper looks at these challenges in more detail and also overviews more recent studies focusing on this topic, as well as current state-of-the-art methods for bypassing scalability issues. It also gives useful information for researchers and practitioners working on the development of generative AI.