arXiv:2510.03243v3 Announce Type: replace Abstract: Efficient scheduling of large language model (LLM) inference tasks is critical for achieving low latency and high throughput, a challenge that is becoming increasingly acute with the rise of reasoning-capable LLMs whose generation lengths are highly variable. Traditional strategies like First Come, First-Serve (FCFS) often suffer from Head-of-Line (HOL) blocking, where long-running tasks delay shorter ones queued behind them. In this paper, we introduce PARS, a prompt-aware LLM task scheduler that mitigates HOL blocking by approximating short
The increasing complexity and variable generation lengths of reasoning-capable LLMs necessitate more efficient scheduling, making solutions like PARS critical for maintaining performance.
Improved LLM scheduling directly impacts the cost and efficiency of AI inference, a foundational aspect of current and future AI applications, influencing accessibility and scalability.
This advancement changes how LLM inference tasks are managed, moving beyond basic FCFS to more intelligent systems that reduce latency and increase throughput for complex models.
- · AI compute providers
- · LLM developers
- · Cloud AI service providers
- · Researchers in AI systems
- · Inefficient LLM serving infrastructures
- · Systems heavily reliant on FCFS scheduling
Reduced latency and increased throughput for complex LLM tasks on existing hardware.
Lower operational costs for AI companies and broader deployment of advanced LLMs.
Accelerated development of even more complex and variable-length AI models as serving bottlenecks are mitigated.
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