As large language models move from experimentation to production, the backend infrastructure supporting them becomes a critical bottleneck. This engineering deep-dive explores how to design a high-concurrency request scheduling system and implement resource isolation for LLM applications. Key patterns include hierarchical priority queues that prevent starvation of critical requests, dynamic rate limiting based on model load, and tenant-level resource partitioning using cgroups or container orchestration. The article also discusses trade-offs between fairness and throughput, and how to handle burst traffic without degrading service quality. For engineering teams building or operating LLM serving platforms, these patterns offer a practical blueprint for achieving stable, predictable performance under load. The insights are particularly relevant for multi-tenant environments where different users or applications compete for GPU and memory resources. By adopting these scheduling and isolation strategies, teams can reduce tail latency, improve resource utilization, and ensure consistent user experience.
This article presents practical engineering patterns for building the backend infrastructure of large language model applications, specifically addressing high-concurrency request scheduling and resource isolation. It covers techniques like priority queues, rate limiting, and tenant-level resource partitioning to ensure stable and fair LLM serving. The content is valuable for teams deploying LLMs in production environments where multi-tenancy and performance predictability are critical.