PartInfer, published at OpenReview, presents neuron-level optimization for edge-device LLM inference. Instead of treating the model as a uniform graph, PartInfer identifies "important" and "less important" neurons via gradient analysis, and applies different optimization (quantization, pruning, KV-cache strategy) to each. The result is significantly better quality-efficiency trade-off on edge devices.