Model quantization reduces the precision of neural network parameters from high-bit representations (typically 32-bit or 16-bit floating point) to lower-bit formats (such as 8-bit or 4-bit integers), enabling faster inference, reduced memory footprint, and lower computational costs while maintaining acceptable accuracy. This technique has become essential for deploying large language models (LLMs) and deep learning models on resource-constrained devices, from edge hardware to consumer GPUs. Understanding the trade-offs between quantization granularity (per-tensor, per-channel, per-group), calibration methods (min-max, entropy-based, percentile), and algorithmic approaches (post-training vs quantization-aware training) is critical for practitioners seeking to optimize model deployment without sacrificing performance beyond acceptable thresholds.