What is the role of quantization in improving LLM inference efficiency?

Quantization plays a crucial role in improving LLM inference efficiency by reducing the model size and computational requirements without significantly sacrificing performance. It works by converting the model's weights and activations from high-precision formats (like float32) to lower-precision formats (such as int8), which decreases memory usage and speeds up arithmetic operations. For instance, research has shown that quantized models can achieve up to 4x faster inference times while maintaining accuracy levels comparable to their full-precision counterparts, as demonstrated in studies like "Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference" by Jacob et al. (2018).