Summary
This document is a guide focused on the sizing requirements for deploying Large Language Models (LLMs). It outlines the computational needs for running LLMs, emphasizing the importance of understanding customer requirements to design effective systems. The guide details the computational requirements for both inferencing and fine-tuning/training LLMs, providing formulas to estimate GPU memory needs based on model size and quantization factors. It also discusses the significance of gathering specific requirements from customers, such as model selection, input and output tokens, average requests per second, and latency requirements. By addressing these factors, solution architects can optimize system design to meet customer expectations while ensuring efficient resource utilization. The guide includes practical examples and methodologies, such as the Rule of Thumb for estimating GPU memory requirements, and explains the role of KV caching in enhancing inference efficiency. Overall, it serves as a comprehensive resource for understanding and implementing LLM deployments.
