Which application provides tools for tracking latency and throughput to LLMs?

The correct choice is Triton Inference Server, which is specifically designed for deploying, managing, and scaling machine learning models in production environments. It offers advanced features for monitoring model performance, including tools to track latency and throughput metrics for large language models (LLMs). This ability to measure how long it takes for a model to respond to requests (latency) and how many requests it can handle in a given timeframe (throughput) is crucial for optimizing performance and ensuring smooth operation in real-time applications.

Other options, while valuable in their respective contexts, do not focus specifically on latency and throughput tracking for LLMs. TensorBoard is mainly used for visualizing training metrics and performance tracking during model training. Pandas Profiling provides an exploratory data analysis tool for summarizing dataframes, which aids in understanding data rather than monitoring model metrics. Keras Tuner is focused on hyperparameter tuning for models, and does not provide the specialized monitoring features essential for assessing LLM latency and throughput. Thus, Triton Inference Server stands out as the most relevant tool for the task described.