Sinno Jialin Pan

Mixture-of-Experts (MoE) models offer dynamic computation, but are typically deployed as static full-capacity models, missing opportunities for deployment-specific specialization. We introduce PreMoE, a training-free framework that proactively compiles sparse MoE variants for targeted deployment scenarios. At its core is Predicted Expert Utility (PEU), a robust metric for estimating expert importance from router logits through high-confidence threshold filtering and logit transformation, which together stabilize utility estimation under aggressive sparsity. Using PEU scores computed on a small calibration set, PreMoE produces domain-aware expert rankings that can be used to compile either domain-specific specialists or high-efficiency multi-domain generalists, without any retraining. Across MoE models ranging from 30B to 718B parameters, PreMoE preserves near-full accuracy at model-dependent sparsity levels of up to 50\%; on DeepSeek-R1, 50\% sparsity halves the deployment NPU count and increases throughput by 23\%. Controlled studies across calibration sources, formats, random subsets, and domain mixtures confirm that these gains are stable rather than tied to a particular calibration set. PreMoE further exposes a practical deployment trade-off: specialists maximize in-domain efficiency, while synthesized generalists retain broader cross-domain capability at the same sparsity budget.

Scaling large language models (LLMs) improves performance but significantly increases inference costs, with feed-forward networks (FFNs) consuming the majority of computational resources. While Mixture-of-Experts (MoE) architectures can reduce this cost through sparse activation, restructuring ex...