Dynamic Quantization

Dynamic quantization aims to improve the efficiency of deep learning models by representing their weights and activations using fewer bits, thereby reducing computational cost and memory footprint. Current research focuses on optimizing quantization strategies for various model architectures, including large language models (LLMs), diffusion models, and vision transformers, often employing techniques like per-tensor or per-token quantization, dynamic bit allocation, and weight dilation to mitigate accuracy loss. These advancements are significant for deploying large models on resource-constrained devices and accelerating inference speed in diverse applications such as natural language processing, image generation, and video processing.