ICLR 2026poster0 citations

Reasoning Language Model Inference Serving Unveiled: An Empirical Study

Qi Li, Junpan Wu, Xiang Liu, Yuxin Wang, Zeyu Li, Zhenheng Tang, Yuhan Chen, Shaohuai Shi

Abstract

The reasoning large language model (RLLM) has been proven competitive in solving complex reasoning tasks such as mathematics, coding, compared to traditional LLM. However, the serving performance and behavior of RLLM remains \textit{unexplored}, which may undermine the deployment and utilization of RLLM in real-world scenario. To close this gap, in this paper, we conduct a comprehensive study of RLLM service. We first perform a pilot study on comparing the serving performance between RLLM and traditional LLM and reveal that there are several distinct differences regarding serving behavior: (1) \textit{significant memory usage and fluctuations}; (2) \textit{straggler requests}; (3) \textit{adaptive running time}; (4) \textit{domain preference}. Then we further investigate whether existing inference optimization techniques are valid for RLLM. Our main takeaways are that model quantization methods and speculative decoding can improve service system efficiency with small compromise to RLLM accuracy, while prefix caching, KV cache quantization may even degrade accuracy or serving performance for small RLLM. Lastly, we conduct evaluation under real world workload modeled by Gamma distribution to verify our findings. Empirical results for real world workload evaluation across different dataset are \textit{aligned} with our main findings regarding RLLM serving. We hope our work can provide the research community and industry with insights to advance RLLM inference serving.

Reasoning Large Language ModelLLM Serving
BibTeX
@inproceedings{
li2026reasoning,
title={Reasoning Language Model Inference Serving Unveiled: An Empirical Study},
author={Qi Li and Junpan Wu and Xiang Liu and Yuxin Wang and Zeyu Li and Zhenheng Tang and Yuhan Chen and Shaohuai Shi and Xiaowen Chu},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https://openreview.net/forum?id=6CGjZYp6ft}
}
Reasoning Language Model Inference Serving Unveiled: An Empirical Study · ICLR 2026