This paper proposes an empirical methodology to study software aging in GPU-based LLM serving systems. Traditional aging studies focus on CPU-centric software with relatively regular workloads; LLM serving is different, spanning a Python host and a CUDA device, handling requests whose cost varies by orders of magnitude, and relying on rapidly evolving software stacks. We run a 216-hour campaign across six co-located deployments under identical stress conditions, monitor host, device, and client metrics in parallel, and apply a statistical pipeline that accounts for autocorrelation and multiple testing. Our results reveal statistically significant memory aging in all deployments, with leak rates strongly dependent on the serving runtime and deployment configuration. Beyond these findings, we provide a reproducible framework that opens a research direction at the intersection of the software aging and rejuvenation and LLM serving communities.

翻译：本文提出了一种实证方法来研究基于GPU的大语言模型服务系统中的软件老化。传统的老化研究聚焦于以CPU为中心的软件,其工作负载相对规则；而大语言模型服务则不同,它横跨Python主机与CUDA设备,处理成本差异达数个数量级的请求,并依赖于快速迭代的软件栈。我们在相同压力条件下对六个共置部署开展了216小时的实验,并行监控主机、设备及客户端指标,并应用了考虑自相关性与多重检验的统计流程。实验结果显示,所有部署均存在统计上显著的内存老化,泄漏速率高度依赖于服务运行时与部署配置。除上述发现外,我们还提供了一个可复现的框架,为软件老化与更新领域及大语言模型服务领域的交叉研究开辟了新方向。