The project automates performance estimation for large language models by computing FLOPs, memory usage, and latency based on model, hardware, data type, and parallelism settings. It provides both a Python API (LLMAnalysis class) and command-line entry points for quick calculations. The tool covers parallelism schemes, activation recomputation, data types, and inference assumptions to help ML engineers plan resource requirements for LLM training and serving.