The article provides an overview of quantization in the context of large language models (LLMs), emphasizing its importance for enhancing throughput, reducing memory usage, maintaining accuracy, and managing costs. Quantization, a process of converting continuous values to discrete sets, is crucial for optimizing inference engines. It explores various quantization techniques, including post-training quantization (PTQ), and explains key methods like SmoothQuant and Activation Aware Quantization (AWQ) that address challenges posed by LLMs' size and complexity. Additionally, it clarifies misconceptions, such as GGUF being a file format rather than a quantization method, and highlights the significance of hardware compatibility in the quantization process. The article underscores the role of quantization in making LLMs more efficient and accessible, encouraging further exploration of the topic for practical applications.