End-to-End Observability for vLLM and TGI: from DCGM to Tokens
This article details how to achieve end-to-end observability for large language model inference servers like vLLM and TGI. It highlights that standard observability tools fall short due to unique LLM serving characteristics such as variable latency, dynamic batching, and the critical role of the KV cache. The author proposes a layered approach, correlating user-facing token rendering with underlying GPU silicon metrics, and provides specific signals to monitor at each layer, from business costs down to GPU hardware. AI
IMPACT Provides engineers with a framework to monitor and optimize LLM inference performance, crucial for production deployments.
