Generative AI, the branch of artificial intelligence (AI) that is designed to generate new data, images, code, or other types of content that humans do not explicitly program, is rapidly becoming pervasive across nearly all facets of business and technology.
Earlier this year, Dell Technologies and NVIDIA introduced a groundbreaking project for generative AI, with a joint initiative to bring generative AI to the world’s enterprise data centers. This project delivers a set of validated designs for full-stack integrated hardware and software solutions that enable enterprises to create and run custom AI Large Language Models (LLMs) at scale using unique data that is relevant to their own organization.
An LLM is an advanced type of AI model that has been trained on an extensive dataset that can understand, process, and generate natural language text. However, AI built on public or generic models is often not well suited for an enterprise to use in their business. Public AI solutions may be trained on outdated or limited datasets, which can impact the accuracy and relevance of the content generated and make it challenging to maintain up-to-date and high-quality content for businesses that require current and precise information. Enterprise use cases require domain-specific knowledge to train, customize, and operate their LLMs.
Model customization is the process of retraining an existing or foundation generative AI model for task-specific or domain-specific use cases. For large models, it is more efficient to customize a foundation model than to train a model from the beginning. Some customization techniques in use today include fine-tuning, instruction tuning, prompt learning (including prompt tuning and P-tuning), reinforcement learning with human feedback, transfer learning, and use of adapters (or adaptable transformers).
Dell Technologies and NVIDIA have designed a scalable, modular, and high-performance architecture that enables enterprises everywhere to create a range of generative AI solutions that apply to their businesses, reinvent their industries, and give them a competitive advantage.
This design for model customization is the second in a series of validated designs for generative AI that focus on all facets of the generative AI life cycle, including inferencing, model customization, and model training. While these designs are focused on generative AI use cases, the architecture is more broadly applicable to more general AI use cases such as predicitive AI.