LLM Inference Performance Benchmarking from Scratch (phillippe.siclait.com)

A recent article delves into the intricacies of benchmarking Large Language Model (LLM) inference performance, emphasizing the need for accurate metrics to enhance efficiency and decrease the environmental impact of these systems. The author outlines a step-by-step process to create a Python benchmarking script, covering key stages like data generation, load generation, response processing, and performance analysis. Crucial performance metrics such as Time-to-First-Token (TTFT), Inter-Token Latency (ITL), and throughput are defined and computed, enabling developers to assess both latency and throughput under various loads. This work is particularly significant for the AI/ML community as it provides a foundational framework for performance engineering LLMs, essential for optimizing resources while maintaining output quality. By detailing the methodology for benchmarking, the piece offers insights into measuring LLM performance in realistic scenarios. Furthermore, it encourages further exploration into production-level tools like NVIDIA's AIPerf, paving the way for improved benchmarking practices in the evolving landscape of AI-driven applications.