llama-benchy is a command-line benchmarking tool designed to evaluate the performance of language models across various backends, supporting any OpenAI-compatible endpoint. Unlike traditional benchmarking tools, it measures prompt processing and token generation speeds at different context lengths, allowing for a more nuanced understanding of model performance. It offers features like configurable prompt length, generation length, and context depth, and uses HuggingFace tokenizers for accurate token counts. This tool addresses limitations in existing benchmarking solutions by providing detailed metrics such as time to first response and end-to-end time to first token, making it highly useful for developers working with multiple inference engines. Why this matters: It enables developers to comprehensively assess and compare the performance of language models across different platforms, leading to more informed decisions in model deployment and optimization.
