Large language models (LLMs) are impressive feats of artificial intelligence, capable of generating human-like text, translating languages, and answering questions with remarkable accuracy. But their massive size presents a challenge: they're resource-intensive, requiring significant computational power and storage. To address this, researchers have developed techniques like quantization, which compresses the models by reducing the precision of their internal numerical representations (think of it like rounding off numbers). While this makes LLMs more efficient, a new study explores a critical question: does quantization affect the models' confidence in their predictions? The research delves into how 4-bit quantization, using a method called GPTQ, impacts the calibration and confidence of several popular LLMs, including BLOOM, OPT, Mistral, and LLaMA, across various scales. The findings reveal that quantization generally decreases the models' confidence in correct answers, with the effect varying across different models and scales. Interestingly, the study found that quantization disproportionately affects predictions where the full-precision model already exhibited low confidence. This suggests that the compression process exacerbates existing uncertainties within the model. The implications of these findings are significant. If quantization makes LLMs less sure of their correct answers, it could impact their reliability in critical applications. Further research is needed to explore methods for mitigating this confidence loss, potentially by incorporating calibration techniques into the quantization process itself. This could pave the way for more efficient and robust LLMs, enabling their wider deployment in real-world scenarios.