arXiv:2606.18431v1 Announce Type: new Abstract: LLM serving exhibits extreme length variability, making size-based scheduling difficult in practice. Recent LLM schedulers approximate SJF/SRPT using predicted decode lengths or ranks and primarily report mean-centric metrics such as TTFT and TBT. We show that these prediction-driven policies can be fragile under distribution shifts, bursty arrivals, and GPU memory pressure, while offering limited control over the tail latency (P90-P99) that dominates user experience, even with perfect decode-length knowledge. We introduce a distribution-aware, p
The proliferation of LLMs and their diverse applications, coupled with increasing demand and performance requirements, drives the immediate need for more efficient and robust scheduling solutions.
Improving LLM inference scheduling directly translates to better user experience, lower operational costs, and more efficient utilization of critical compute infrastructure.
Current prediction-driven LLM scheduling policies are shown to be fragile, and new distribution-aware methods are being developed to offer better control over critical tail latencies.
- · Cloud service providers
- · LLM developers
- · Enterprise AI users
- · Hardware manufacturers (GPUs)
- · Inefficient LLM inference platforms
- · Companies with high latency-sensitive LLM applications
Improved performance and reliability of large language model serving infrastructures.
Reduced operational costs for AI companies and increased accessibility of advanced LLM capabilities for a wider range of applications.
Further acceleration of AI adoption in latency-sensitive applications, potentially leading to new business models and services.
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Read at arXiv cs.LG