Quantization

Understand LLM Quantization for Efficient Inference

Large language models can require substantial memory bandwidth and compute during inference. Quantization reduces those requirements by representing some model values with fewer bits than the floating-point formats commonly used during training. The idea sounds simple: store numbers with lower precision. In practice, useful quantization is a trade-off among memory, latency, hardware support, implementation complexity, and model quality. Understanding that trade-off helps you choose a configuration based on measurements instead of assuming that fewer bits are always better.