arXiv:2602.02061v2 Announce Type: replace Abstract: Explosive demands for LLMs often cause user queries to accumulate in server queues, requiring efficient routing (query-LLM matching) and scheduling (query prioritization) mechanisms. Several online algorithms are being deployed, but they overlook the following two key challenges inherent to conversational LLM services: (1) unsatisfied users may retry queries, increasing the server backlog, and (2) requests for ``explicit" feedback, such as ratings, degrade user experiences. In this paper, we develop a joint routing and scheduling algorithm th
The explosive demand for LLMs is creating significant backlogs and user dissatisfaction, necessitating immediate algorithmic solutions for resource management.
Efficient routing and scheduling of LLMs are critical for scaling AI services, improving user experience, and retaining market share in a highly competitive and resource-constrained environment.
New algorithms that account for user retrials and avoid explicit feedback requests will optimize LLM infrastructure, potentially leading to more seamless and scalable AI services.
- · Cloud providers offering LLM services
- · Companies developing LLM router/scheduler software
- · Users of LLM-powered applications
- · LLM service providers with inefficient queueing systems
- · Companies relying on explicit user feedback for model improvement
Improved user satisfaction and reduced operational costs for LLM providers due to more efficient resource utilization.
Accelerated adoption of LLM-powered applications as performance and responsiveness improve, driving further demand for compute infrastructure.
The necessity for sophisticated resource management in AI becomes a new standard, influencing the design of future AI systems and potentially leading to specialized 'AI operations' sectors.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG