The growing scale of large language models (LLMs) not only demands extensive computational resources but also raises environmental concerns due to their increasing carbon footprint. Model quantization emerges as an effective approach that can reduce the resource demands of LLMs by decreasing parameter precision without substantially affecting performance (e.g., 16 bit$\rightarrow 4$bit). While recent studies have established quantization as a promising approach for optimizing large code models (LCMs)-a specialized subset of LLMs tailored for automated software engineering-their findings offer only limited insights into its practical implications. Specifically, current investigations only focus on the functional correctness of the code generated by quantized models, neglecting the gap that remains in understanding how quantization impacts critical aspects of quality attributes of the code, such as reliability, maintainability, and security. To bridge this gap, our study investigates the effects of quantization on the qualitative aspects of automatically generated code. We apply Activation-aware Weight Quantization (AWQ) to two widely used code models-CodeLlama and DeepSeekCoder-to generate Java and Python code. Leveraging state-of-the-art static analysis tools, we evaluate software quality metrics and static features, including cyclomatic complexity, cognitive complexity, and lines of code (LoC). Our findings reveal that quantization not only establishes itself as a robust technique capable of withstanding functional challenges-producing code that passes test cases at rates comparable to non-quantized models-but also preserves key qualitative attributes and static features often sought after by developers, such as maintainability and structural complexity.