Quantization

Quantization is a model compression technique that reduces the precision of neural network weights and activations from high-precision floating-point numbers to lower-precision representations, significantly decreasing memory usage and computational requirements. This process converts 32-bit or 16-bit floating-point values to 8-bit integers or even lower precision formats while maintaining acceptable model performance through careful calibration and optimization strategies.

Quantization enables deployment of large language models on resource-constrained devices, reduces inference latency, and lowers energy consumption without requiring architectural changes. The technique encompasses various approaches including post-training quantization, quantization-aware training, and dynamic quantization that balance compression ratio with accuracy preservation. Advanced quantization methods incorporate techniques like mixed-precision quantization, outlier-aware quantization, and structured pruning to optimize efficiency while minimizing performance degradation. Quantization serves as a critical enabler for edge AI deployment, mobile applications, and cost-effective large-scale inference scenarios.