Floating Point Quantization

Floating-point quantization aims to reduce the memory footprint and computational cost of deep learning models, particularly large language models (LLMs) and diffusion models, by representing their weights and activations using fewer bits than the standard 32-bit precision. Current research focuses on optimizing low-bit floating-point formats (e.g., FP8, FP6, FP4) for various architectures, including transformers and U-Nets, exploring techniques like asymmetric quantization and per-channel scaling to mitigate accuracy loss. These advancements are crucial for deploying large models on resource-constrained devices and accelerating inference speed, impacting both the efficiency of AI research and the accessibility of powerful AI applications.