Home > AI Solutions > Gen AI > White Papers > Maximizing AI Performance: A Deep Dive into Scalable Inferencing on Dell with NVIDIA > Overview
Generative AI is a branch of artificial intelligence (AI) designed to generate new data, images, code, or other types of content that humans do not explicitly program, and it has become integral across nearly all facets of business and technology. Inferencing is the process of using a trained AI model to generate predictions that make decisions or produce outputs based on input data. It plays a crucial role in generative AI as it enables the practical application and real-time generation of content or responses. Properly designed and managed inferencing allows for near-instant content creation and interactive experiences with resource efficiency, scalability, and contextual adaptation. This capability supports applications ranging from chatbots and virtual assistants to context-aware natural language generation and dynamic decision systems.
In 2023, Dell and NVIDIA introduced a comprehensive full-stack integrated hardware and software solution aimed to revolutionize enterprise data centers with generative AI capabilities. This pioneering solution enables enterprises to run AI models on-premises, offering benefits including enhanced data security, greater control over data, and reduced exposure to external networks. By bringing AI closer to the data source, enterprises can achieve better data governance, stringent compliance control, and the ability to implement bespoke security measures tailored to their specific infrastructure and environment.
The scalable, modular, and high-performance architecture designed by Dell and NVIDIA enables enterprises to develop generative AI solutions tailored to their specific business needs, reinventing their industries and providing a competitive edge. This reference design shows the inferencing scale with NVIDIA NIM containers in Kubernetes using Llama 3 on PowerEdge XE9680 and R760xa, which are part of Dell AI Factory with NVIDIA. We measure performance using NVIDIA GenAI Perf. We explore concurrency, review model metrics at scale, and analyze model quantization. Additionally, we will demonstrate clustering that spans multiple servers, scaling loads, and deploying different types of models simultaneously on a PowerEdge server.