InferOpt: Constrained Multi-Objective Search for LLM Inference Configurations
Serving an LLM means setting dozens of inference-time knobs, from per-layer KV retention to per-layer expert counts. Practice sets them with mechanism-specific heuristics that return a single operating point and do not scale to layer-wise search spaces. We recast inference configuration as constrained multi-objective black-box optimization and build InferOpt, a reusable search framework that requires only variable bounds, a deterministic resource cost, and an evaluation hook. InferOpt searches on a frozen sampled proxy set, rejects over-budget candidates before any model call, tightens the budget adaptively, and re-validates Pareto representatives on full-scale dataset. One pipeline cover...
| Authors | Qi Chen, Yingying Cheng, Zhaoyi Sun et al. |
|---|---|
| Primary category | cs.AI |
| Published | 3 October 2026 |
| Last updated at source | 3 October 2026 |
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