With the increasing complexity of generative AI models, post-training quantization (PTQ) has emerged as a promising solution for deploying hyper-scale models on edge devices such as mobile devices and TVs.Existing PTQ schemes, however, consume considerable time and resources, which could be a bottleneck in real situations where frequent model updates and multiple hyperparameter tunings are required.As a cost-effective alternative, learning-free PTQ schemes have been proposed. Still, the performance is somewhat limited because they cannot consider inter-layer dependency within the attention module, a significant feature of Transformers.In this paper, we thus propose a novel PTQ algorithm that balances accuracy and efficiency.The key idea of the proposed algorithm called aespa is to perform quantization layer-wise for efficiency while considering cross-layer dependency to preserve the attention score.Through extensive experiments on various language models and complexity analysis, we demonstrate that aespa is accurate and efficient in quantizing Transformer models.