As the use of large language models (LLMs) grows across many applications, such as chatbots and content creation, it’s important to understand the process of scaling and optimizing inference systems to make informed decisions about hardware and resources for LLM inference.
In the following talk, Dmitry Mironov and Sergio Perez, senior deep learning solutions architects at NVIDIA, guide you through the critical aspects of LLM inference sizing. Sharing their expertise, best practices, and tips, they walk you through how to efficiently navigate the complexities of deploying and optimizing LLM Inference projects.
Follow along with a PDF of the session, while learning how to choose the right path for your AI project by understanding key metrics in LLM inference sizing. Discover how to accurately size hardware and resources, optimize performance and costs, and select the best deployment strategies, whether on-premises or in the cloud.
You will also cover advanced tools like the NVIDIA NeMo inference sizing calculator (use this NIM for LLM benchmarking guide to replicate it) and NVIDIA Triton performance analyzer, enabling you to measure, simulate, and improve your LLM inference systems.
By applying their practical guidelines and improving your technical skill set, you’ll be better equipped to tackle challenging AI deployment scenarios and achieve success in your AI initiatives.
Watch the talk LLM Inference Sizing: Benchmarking End-to-End Inference Systems, explore more videos on NVIDIA On-Demand, and gain valuable skills and insights from industry experts by joining the NVIDIA Developer Program.
This content was partially crafted with the assistance of generative AI and LLMs. It underwent careful review and was edited by the NVIDIA Technical Blog team to ensure precision, accuracy, and quality.
