Different Quantization

Different quantization techniques aim to reduce the computational cost and memory footprint of large language models (LLMs) and other deep neural networks without significant accuracy loss. Current research focuses on developing novel quantization methods, including mixed-precision approaches that assign different bit-widths to various model parameters (e.g., weights, activations, and key/value caches), and exploring optimal quantization strategies for specific model architectures like Vision Transformers and LLMs. These advancements are crucial for deploying large models on resource-constrained devices and improving the efficiency of AI applications across various domains.