Quantization reduces the precision of model weights (e.g., from 32-bit floats to 8-bit or even lower) to speed up inference and decrease memory usage. It can slightly degrade performance but significantly improves deployment efficiency on resource-constrained devices. Post-training quantization and quantization-aware training are two main strategies.