When the key-value (KV) cache fills, a serving engine must choose which request to evict. We present PreemptBench, comparing seven preemption-target policies across seven reasoning-model families and two benchmarks. Our service-level objective (SLO) breach rate counts every accepted request, including late completions, post-acceptance failures, and unfinished work; this survivorship correction prevents deferred backlog from being omitted from the metric. In the low-breach regime, where last-in, first-out (LIFO) breach is below 15%, young-target-biased policies form a lower-breach cluster than policies without this bias, including random and largest-KV, consistent with a stable incumbent cohort; graded eviction depth is secondary. Within a pure remaining-decode rule, replacing the online predictor with a privileged empirical statistical length oracle, constructed from prior temperature-zero runs, yields no significant gain; neither significantly outperforms LIFO. It summarizes a prompt-conditioned length distribution, not the current execution’s realization, and is not SLO-optimal. Finally, the uncensored single-stream completion-time tail defines a conceptual feasibility floor, but our traces provide only a censored low-load proxy. Post-hoc rescoring across 600–1800-second deadlines finds aggregate memory-freeing reversals at tighter SLOs, but none at 1800 seconds. Thus LIFO is a strong conditional baseline, not a universal optimum.

With the rapid growth of Large Language Models (LLMs) across various domains, numerous new LLMs have emerged, each possessing domain-specific expertise. This proliferation has highlighted the need for quick, high-quality, and cost-effective LLM query response methods. Yet, no single LLM exists to efficiently balance this trilemma. Some models are powerful but extremely costly, while others are fast and inexpensive but qualitatively inferior. To address this challenge, we present TO-Router, a non-monolithic LLM querying system that seamlessly integrates various LLM experts into a single query interface and dynamically routes incoming queries to the most high-performant expert based on query’s requirements. Through extensive experiments, we demonstrate that when compared to standalone expert models, TO-Router improves query efficiency by up to 40%, and leads to significant cost reductions of up to 30%, while maintaining or enhancing model performance by up to 10%.

Large language models (LLMs) have surged in popularity and are extensively used in commercial applications, where the efficiency of model serving is crucial for the user experience. Most current research focuses on optimizing individual sub-procedures, e.g. local inference and communication, however, there is no comprehensive framework that provides a holistic system view for optimizing LLM serving in an end-to-end manner. In this work, we conduct a detailed analysis to identify major bottlenecks that impact end-to-end latency in LLM serving systems. Our analysis reveals that a comprehensive LLM serving endpoint must address a series of efficiency bottlenecks that extend beyond LLM inference. We then propose ScaleLLM, an optimized system for resource-efficient LLM serving. Our extensive experiments reveal that reveal that with 64 concurrent requests on Mixtral 8x7B, ScaleLLM achieves a 4.3× speed up over vLLM and outperforms state-of-the-arts with 1.5× higher throughput.