The Price of Precision: A Calibrated Cost Model for Mixed-Precision LLM Serving

Abstract:Choosing a mixed-precision configuration for large language model (LLM) serving trades speed against accuracy. Allocators that use average bit-width or a memory budget as a speed proxy can miss changes in the resource that limits execution. We argue that served speed can be limited by compute, weight loading, or key-value (KV) cache access, depending on the phase, batch size, and context length. A byte-based proxy does not capture all of these costs, so fewer bits do not always mean faster serving. To address this gap, we build a cost model that accounts for the binding resource and use it in two ways. First, as a predictor: given a configuration and operating point, it estimates serving latency using constants calibrated for the model, GPU, and serving stack. We evaluate its accuracy across three hardware classes. Second, as an allocator: we combine modeled time costs with measured per-tensor accuracy costs and use a dynamic program to select a configuration under a latency or accuracy budget. We compare its configurations against byte-aware and sensitivity-only allocation.

Machine Learning (cs.LG)

arXiv:2610.15629 [cs.LG]

(or arXiv:2610.15629v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.15629 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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