Metron 是一个评估大型语言模型 (LLM) 推理系统性能的全新框架，它超越了传统的指标，如 TTFT 和 TBT，更全面地捕捉了实时交互中的用户体验。Metron 引入了新的指标，例如流畅度指数和流畅令牌生成率，这些指标考虑了令牌生成的时间方面，更准确地反映了用户感知的性能。该框架针对开源和专有 LLM 推理系统进行了评估，结果表明 Metron 在揭示性能差异和确保真实世界应用中更好的用户体验方面非常有效。

😊 **Metron 框架的背景：** 传统的评估 LLM 推理系统性能的指标，如 TTFT（第一个令牌的时间）和 TBT（令牌之间的时间），无法捕捉到实时交互中的完整用户体验。这在聊天和翻译等应用中至关重要，因为响应速度直接影响用户满意度。因此，需要一个更细致的评估框架来全面涵盖 LLM 推理的复杂性，以确保在现实场景中最佳部署和性能。

🤔 **Metron 的关键指标：** Metron 引入了新的指标，例如流畅度指数和流畅令牌生成率，这些指标专门针对实时、流式 LLM 交互的细微差别。这些指标考虑了令牌生成的时间方面，从而确保更准确地反映用户感知的性能。流畅度指数通过设置令牌级截止时间并衡量满足截止时间的比例，提供了对用户体验约束的精确定义。

💪 **Metron 的应用和结果：** 该框架评估了开源和专有 LLM 推理系统，并使用流畅度指数来衡量满足截止时间的百分比，并根据实时性能动态调整截止时间。该方法提供了对系统处理用户请求的能力的全面了解，而不会影响响应能力。例如，对 vLLM 和 Sarathi-Serve 等系统的评估表明，Sarathi-Serve 达到了更少的截止时间遗漏和更高的流畅度。

🚀 **Metron 的未来影响：** Metron 为评估 LLM 推理系统提供了一种更准确的方法，揭示了传统指标无法捕捉到的用户体验方面的重大差异。该框架对于改进 LLM 推理系统，确保在现实世界应用中提供更好的用户体验具有重要意义。

🌟 **Metron 的创新之处：** Metron 的创新之处在于它将用户体验作为评估 LLM 推理系统性能的关键指标。它不仅关注模型的计算效率，更关注用户在实际使用过程中感受到的流畅度和响应速度。这种以用户为中心的评估方法为 LLM 推理系统的优化提供了新的方向，也为未来 AI 系统的评估标准设定了更高的要求。

Evaluating the performance of large language model (LLM) inference systems using conventional metrics presents significant challenges. Metrics such as Time To First Token (TTFT) and Time Between Tokens (TBT) do not capture the complete user experience during real-time interactions. This gap is critical in applications like chat and translation, where responsiveness directly affects user satisfaction. There is a need for a more nuanced evaluation framework that fully encapsulates the intricacies of LLM inference to ensure optimal deployment and performance in real-world scenarios.

Current methods for evaluating LLM inference performance include TTFT, TBT, normalized latency, and Time Per Output Token (TPOT). These metrics assess various aspects of latency and throughput but fall short in providing a comprehensive view of the user experience. For example, TTFT and TBT focus on individual token latencies without considering end-to-end throughput, while normalized metrics obscure issues like inter-token jitter and scheduling delays. These limitations hinder their effectiveness in real-time applications where maintaining a smooth and consistent token generation rate is crucial.

A team of researchers from Georgia Institute of Technology, Microsoft Research India, and Intel AI Lab propose Metron, a comprehensive performance evaluation framework. Metron introduces novel metrics such as the fluidity-index and fluid token generation rate, which capture the nuances of real-time, streaming LLM interactions. These metrics consider the temporal aspects of token generation, ensuring a more accurate reflection of user-facing performance. By setting token-level deadlines and measuring the fraction of deadlines met, the fluidity-index provides a precise definition of user experience constraints. This approach represents a significant contribution by offering a more accurate and user-centric evaluation method.

Metron’s fluidity-index metric sets deadlines for token generation based on desired TTFT and TBT values, adjusting these based on prompt length and observed system performance. This method accounts for scheduling delays and variable token generation rates, ensuring smooth output. The framework evaluates both open-source and proprietary LLM inference systems, applying the fluidity-index to measure the percentage of deadlines met and dynamically adjusting deadlines based on real-time performance. This method offers a comprehensive view of the system’s capacity to handle user requests without compromising responsiveness.

Metron provides a more accurate evaluation of LLM inference systems compared to conventional metrics. The fluidity-index and fluid token generation rate reveal significant differences in user experience that are not captured by TTFT or TBT alone. For example, the evaluation of systems like vLLM and Sarathi-Serve demonstrated that Sarathi-Serve achieved fewer deadline misses and higher fluidity. The findings show that Sarathi-Serve maintained a fluidity-index > 0.9 for 99% of requests, achieving a throughput of 600 tokens per second, while vLLM showed a 3x worse tail TBT due to generation stalls. This demonstrates Metron’s effectiveness in revealing performance differences and ensuring better user experiences in real-world applications.

In conclusion, this proposed method, Metron, introduces a novel evaluation framework, including the fluidity-index and fluid token generation rate metrics, to better assess LLM inference performance. This approach overcomes the limitations of conventional metrics by providing a user-centric evaluation that captures the intricacies of real-time token generation. The findings demonstrate Metron’s effectiveness in revealing performance differences and its potential impact on improving LLM serving frameworks, ensuring better user experiences in real-world applications.

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