Mohammed E. Fouda

This survey organizes the landscape of mixed-precision quantization frameworks for language models (MXPLMs): it first reviews quantization fundamentals including uniform and non-uniform quantizers, quantization granularity, and widely used post-training quantization methods, then categorizes and compares recent MXPLM frameworks by their bit allocation strategies and precision configurations across weights, activations, and key-value caches, contrasts them with earlier mixed-precision methods for deep neural networks to identify strategies that transfer and those that face challenges in the LM setting, and closes with open issues such as hardware-aware design, activation quantization, and scalable optimization for billion-parameter models.

This survey organizes the landscape of mixed-precision quantization frameworks for language models (MXPLMs): it first reviews quantization fundamentals including uniform and non-uniform quantizers, quantization granularity, and widely used post-training quantization methods, then categorizes and compares recent MXPLM frameworks by their bit allocation strategies and precision configurations across weights, activations, and key-value caches, contrasts them with earlier mixed-precision methods for deep neural networks to identify strategies that transfer and those that face challenges in the LM setting, and closes with open issues such as hardware-aware design, activation quantization, and scalable optimization for billion-parameter models.