Quantizing an ML model involves reducing the precision of its weights, typically from floating-point formats like FP32 or FP16 to integer formats like INT8 or INT4, to improve inference performance by reducing memory access and compute requirements. This process can lead to significant speedups and cost savings, but it also carries a risk of degrading model output quality if not done carefully. The choice of precision depends on the tradeoff between speed and accuracy, with FP16 being a popular default for LLM inference due to its balance of expressiveness and speed. Quantization algorithms can be complex, but successful implementation can lead to substantial performance gains without affecting model outputs significantly.