Cluster, Route, Escalate: Cascaded Framework for Cost-Aware LLM Serving
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Computer Science > Performance
Title:Cluster, Route, Escalate: Cascaded Framework for Cost-Aware LLM Serving
Abstract:Efficient deployment of large language models (LLMs) in production forces a trade-off between accuracy and cost. Operators often default to a single model that is either expensive for easy queries or insufficient for hard ones. To address this challenge, we propose a two-stage cascaded solution. Stage 1 clusters incoming queries and assigns each cluster to its most cost-effective model. The cost budget for this routing process is set by an interpretable hyperparameter, tuned offline. Stage 2 adds a quality estimation (QE) cascade; when an output from Stage 1 is judged low-quality, the query is escalated to a stronger model. This ensures only hard or low-confidence cases reach the expensive models. On the test datasets, the cascaded system retains 97-99% of the strongest model's accuracy while reducing Time Per Output Token (TPOT). It requires only task-correctness labels and adapts to changes in the model pool without manual reconfiguration.
| Subjects: | Performance (cs.PF); Computation and Language (cs.CL) |
| Cite as: | arXiv:2606.27457 [cs.PF] |
| (or arXiv:2606.27457v1 [cs.PF] for this version) | |
| https://doi.org/10.48550/arXiv.2606.27457
arXiv-issued DOI via DataCite
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