Nvidia has released a new paper titled 'LLM Pruning and Distillation in Practice,' which focuses on the compression of large language models (LLMs) through techniques such as pruning and knowledge distillation. The document aims to make advanced AI models more accessible and cost-effective. Experts, including Dmitry Mironov and Sergio Perez, senior deep learning solutions architects at Nvidia, provide insights into LLM inference sizing, offering best practices for deploying and optimizing LLM projects. The paper includes ten pieces of pruning advice, which cover methods for retraining large LLMs into smaller, more manageable versions. Additionally, a technical session on understanding key metrics for LLM inference sizing is available on Nvidia's On-Demand platform, further supporting the practical application of these findings in real-world settings.