Fine Grained Quantization

Fine-grained quantization aims to reduce the computational cost and memory footprint of large language models (LLMs) and diffusion transformers by representing model weights and activations using fewer bits, without significant performance degradation. Current research focuses on developing efficient quantization schemes, often employing techniques like layer-wise or channel-wise quantization, and integrating them with optimization strategies such as automatic precision search and outlier suppression to mitigate accuracy loss. This work is crucial for deploying these computationally intensive models on resource-constrained devices and improving the efficiency of various applications, including image generation and natural language processing.