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, Xiaowen Chu
2025-10-30
Summary
This paper investigates how well reasoning-focused large language models, or RLLMs, actually *work* in practice when you try to use them, not just how accurate their answers are. While these models are good at complex tasks like math and coding, nobody has really looked at how efficiently they run and how they behave when lots of people are using them at the same time.
What's the problem?
The core issue is that even if a language model is smart, it's not useful if it's too slow or uses too much computer power. RLLMs are designed to be better at reasoning, but it's unknown if this comes at a cost to their performance. Specifically, the researchers noticed RLLMs use a lot of memory, some requests take much longer than others, their processing time changes depending on the task, and they seem to perform better on certain types of problems. No one knew if standard techniques for speeding up language models would even work with these more complex RLLMs.
What's the solution?
The researchers did a bunch of tests to understand how RLLMs behave when serving requests. They compared RLLMs to regular language models and found key differences in how they use resources. Then, they tried out common optimization methods – like reducing the precision of the numbers the model uses and predicting parts of the answer to speed things up – to see if they helped. They found that some methods worked well, improving speed without sacrificing much accuracy, while others actually made things worse. Finally, they tested everything with a realistic workload, simulating many users making requests at once, to confirm their findings.
Why it matters?
This research is important because it gives practical guidance to anyone trying to deploy and use RLLMs in the real world. Knowing how these models behave and which optimization techniques work best will help make them more efficient and affordable, allowing more people to benefit from their reasoning abilities. It provides insights for both researchers working on improving these models and companies trying to build applications using them.
Abstract
The reasoning large language model (RLLM) has been proven competitive in solving complex reasoning tasks such as mathematics, coding, compared to general LLM. However, the serving performance and behavior of RLLM remains 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) significant memory usage and fluctuations; (2) straggler requests; (3) adaptive running time; (4) 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 of real world workload evaluation across different dataset are 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.