RLMServing: A Systematic Empirical Study on Inference Services for Reasoning Language Models

RLMServing is an open-source project accepted by ICLR 2026, which conducts the first large-scale empirical study on inference services for Reasoning Large Language Models (Reasoning LLMs). The project focuses on service bottlenecks and optimization opportunities of reasoning models in production environments, with core objectives including answering key questions such as latency differences between reasoning models and standard models, impacts of batching strategies, efficient memory management, and trade-offs between reasoning depth and latency.

With the rise of reasoning large language models such as OpenAI o1 and DeepSeek-R1, their multi-step internal reasoning (Chain-of-Thought) improves the accuracy of complex tasks, but also brings new service challenges: the reasoning process involves hundreds to thousands of tokens of implicit thinking, leading to a significant increase in Time-to-First-Token (TTFT) latency and higher requirements for GPU memory and computing resources. Traditional research on large model inference services focuses on standard autoregressive models, and there is a lack of research on the service characteristics of reasoning LLMs—RLMServing fills this gap.

RLMServing is an open-source inference service benchmarking framework that provides complete experimental code, data, and configuration files to support reproduction and extension. Its technical modules include: a benchmarking engine (supporting backends like vLLM, TensorRT-LLM, TGI), a workload generator (simulating request distribution based on real dialogue data), a metrics collector (fine-grained monitoring of latency/throughput/memory), and visualization tools (interactive analysis interface).

1. Latency Characteristics: The latency distribution of reasoning models shows a bimodal feature—simple queries have fast responses (first peak), while complex queries require deep reasoning (second peak), which challenges the traditional average latency SLO setting.
2. Batching Strategies: Static batching is prone to head-of-line blocking; continuous batching increases GPU utilization by 30-45% and is the preferred choice; speculative decoding has limited benefits but can accelerate by 15-20% in specific scenarios.
3. Memory Optimization: Reasoning models have high KV Cache usage. The project proposes a dynamic KV Cache compression mechanism that prunes low-importance intermediate states, reducing memory usage by 40% while ensuring quality.

The achievements of RLMServing are of great significance to the AI infrastructure field:

• Cloud Service Providers: Optimize pricing and resource allocation for inference instances, providing more cost-effective solutions;
• Enterprise Developers: Assist in capacity planning to avoid service degradation caused by underestimating reasoning latency;
• Hardware Vendors: Reveal the characteristics of reasoning workloads, providing demand input for the design of next-generation AI chips.

As the first systematic study on reasoning model services, RLMServing has established an important benchmark in this field. With the increase in application scenarios of reasoning LLMs, optimization technologies will continue to evolve. Recommendations: Pay attention to subsequent updates of the project and community optimization solutions; teams deploying reasoning models should refer to the experimental configurations and conduct targeted benchmark tests combined with business scenarios to obtain accurate performance expectations.