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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.
In 2023, 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 delivered 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, typically using deep learning techniques, which is capable of understanding, processing, and generating natural language text. However, AI built on public or generic models is not well suited for an enterprise to use in their business. Enterprise use cases require domain-specific knowledge to train, customize, and operate their LLMs.
LLM training, often called to as pre-training, is the process of training a large-scale neural network model on a vast amount of text data before it is fine-tuned for specific tasks. This pre-training aims to equip the model with a broad understanding of language, including grammar, context, semantics, and common knowledge. The process is fundamental to the creation of foundation models like Llama 2 and GPT (Generative Pre-trained Transformer) and enables them to perform a wide range of language-related tasks, even those they were not explicitly trained for.
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 reference design for training is one in a series of 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 as well.