Practical Strategies for Optimizing LLM Inference Sizing and Performance – NVIDIA Technical Blog News and tutorials for developers, data scientists, and IT admins 2025-07-03T22:20:47Z http://www.open-lab.net/blog/feed/ Michelle Horton <![CDATA[Practical Strategies for Optimizing LLM Inference Sizing and Performance]]> http://www.open-lab.net/blog/?p=87511 2024-09-05T17:57:29Z 2024-08-21T16:00:00Z 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...]]> 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...An illustration of a chatbot.

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��

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