How to calculate the amount of GPU memory needed to support a large language model?
The article discusses how much GPU memory is needed to serve large language models (LLMs), providing valuable insights for developers and researchers in the field of artificial intelligence. It explains that different models require varying amounts of memory depending on their size and architecture, highlighting the complexity of determining the 'right' amount of memory needed. The author includes practical examples of model use, showcasing how performance can be affected by memory allocation. A significant part of the text focuses on analyzing scenarios where GPU resource utilization may vary, stressing that there is no definitive answer to the question of memory requirements. The article encourages optimization of applications and experimentation with different configurations to find the most effective solution for each specific use case.