Model quantization is a transformative approach for optimizing deep neural networks, particularly large language models (LLMs) and vision-language models (VLMs), by reducing the numerical precision of weights and activations. This technique effectively compresses model size and enhances computational efficiency, making it feasible to deploy these resource-intensive models on standard hardware and edge devices. By switching from high-precision formats like FP32 to lower-precision integers such as INT8, quantization can achieve significant memory savings and performance improvements, including a 4× reduction in model size and up to a 16× increase in performance per watt. The process involves choosing appropriate quantization strategies—post-training quantization (PTQ), quantization-aware training (QAT), dynamic and static quantization—and overcoming challenges like handling outliers and ensuring fairness. Recent innovations such as ZeroQAT, FlatQuant, and commutative vector quantization (CommVQ) have pushed the boundaries, enabling low-bit quantization with minimal accuracy loss. These techniques are crucial for making AI more accessible and sustainable, as they reduce energy consumption and inference costs. Platforms like Clarifai integrate these quantization capabilities, providing tools for seamless deployment and monitoring, thus empowering practitioners to efficiently utilize quantized models without deep technical expertise.