arXiv:2606.11916v1 Announce Type: cross Abstract: 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 pipel
The rapid deployment and scaling of GPU-based LLM serving systems make their long-term reliability and performance a critical and immediate concern, prompting studies into software aging.
Understanding software aging in LLM infrastructure directly impacts the stability, cost, and long-term viability of AI applications and services built upon them.
This research provides a methodology to systematically identify and mitigate software aging issues in the complex Python/CUDA stacks used for LLMs, moving beyond CPU-centric analyses.
- · Cloud AI service providers
- · LLM developers
- · Software reliability engineering
- · GPU manufacturers
- · Companies with unreliable AI infrastructure
- · Early-stage unoptimized AI startups
Improved reliability and uptime of large-scale AI serving systems due to better aging management.
Reduced operational costs and higher efficiency for companies running significant LLM inference workloads.
Accelerated adoption and trust in AI systems as their underlying infrastructure becomes more robust and predictable.
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Read at arXiv cs.AI