PostNAS starts with a pre-trained model and freezing its MLP weights, enabling efficient, cost-effective exploration of optimal attention designs without costly retraining. This approach delivers up to 53.6× generation throughput speedup and 6.1× prefilling speedup. It also achieves higher accuracy on MMLU and MMLU-Pro than recent advanced MoE full-attention models, despite their larger scale.