Characterizing Software Aging in GPU-Based LLM Serving Systems
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...
| Authors | Domenico Cotroneo, Bojan Cukic |
|---|---|
| Primary category | cs.SE |
| Published | 10 June 2026 |
| Last updated at source | 10 June 2026 |
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