Companies running large language models face a persistent bottleneck: the memory consumed by key-value caches during inference grows with every token generated, forcing operators to choose between ...

Quantization in neural network inference refers to the process of mapping high-precision parameters and activations to lower-precision representations, typically using integer or even binary values.