A new arXiv paper investigates why large language model performance degrades when processing multiple instances simultaneously, identifying both instance count and context length as key factors. The research systematically analyzes how these variables interact to reduce output quality in multi-instance settings. Findings have direct implications for production deployments where LLMs handle parallel workloads at scale.