5 steps to triage vLLM performance

This blog post outlines a structured approach to diagnosing and improving performance issues with vLLM (a volume optimized Large Language Model) deployments. By defining performance objectives and using specific metrics such as Time to First Token (TTFT) and Inter-Token Latency (ITL), developers can identify bottlenecks in the system's performance, whether it be due to server saturation, memory pressure, or inefficiencies in request handling. Remediation steps include right-sizing models, scaling hardware, utilizing quantization, and refining distribution strategies to enhance overall inference performance.