Quantization reduces the precision of numerical computations in LLMs, such as converting 32-bit floating-point values to 16-bit or 8-bit representations. This decreases the memory footprint and computational requirements, making models more efficient without significantly compromising accuracy. For example, an 8-bit quantized model can perform inference faster and consume less power than its full-precision counterpart.
Quantization is particularly useful for deploying LLMs in resource-constrained environments, such as mobile devices or edge systems. By lowering the hardware requirements, it enables real-time processing and reduces latency. Frameworks like TensorFlow Lite and PyTorch support quantization-aware training, allowing models to maintain higher accuracy despite reduced precision.
In addition to inference efficiency, quantization helps lower the costs of scaling LLMs in large deployments, as it reduces hardware usage and energy consumption. These benefits make quantization an essential technique for balancing performance and efficiency in modern AI systems.