Monitoring LLM Inference with Prometheus and Grafana (vLLM, TGI, Llama.cpp) (www.glukhov.org)

A recent article delves into the critical need for monitoring Large Language Model (LLM) inference workloads using tools like Prometheus and Grafana. As organizations scale beyond single-node setups, traditional API metrics become insufficient, necessitating detailed insights into factors such as latency, queue management, token processing, and cache utilization. This guide emphasizes that while metrics like requests per second are common, LLMs require a more nuanced approach, focusing on token throughput and specific latency types—end-to-end and inter-token. The article provides a comprehensive step-by-step framework to implement monitoring, highlighting practical metrics to track, including p95 latency, token generation rates, and cache usage. It offers examples of how to scrape metrics from popular servers (vLLM, Hugging Face TGI, and llama.cpp) and implement configurations using Docker or Kubernetes. By leveraging Prometheus for data aggregation and Grafana for visualization, practitioners can optimize their LLM deployments, troubleshoot performance bottlenecks, and ensure they meet user expectations. This guide is particularly significant for AI/ML practitioners looking to enhance their observability practices, ensuring they maintain robust LLM performance even under varying loads.