Large language models (LLMs) exhibit amazing performance on a wide variety of tasks such as text modeling and code generation. However, they are also very large. For example Llama 2 70B has 70 billion parameters that require 140GB of memory to store in half precision. This presents many challenges, such as needing multiple GPUs just to serve a single LLM. To address these issues, researchers have developed compression methods that reduce the size of models without destroying performance.
One class of methods, post-training quantization, compresses trained model weights into lower precision formats to reduce memory requirements. For example, quantizing a model from 16 bit to 2 bit precision would reduce the size of the model by 8x, meaning that even Llama 2 70B would fit on a single 24GB GPU. In this work, we introduce QuIP#, which combines lattice codebooks with incoherence processing to create state-of-the-art 2 bit quantized models. These two methods allow QuIP# to significantly close the gap between 2 bit quantized LLMs and unquantized 16 bit models.
Project Page: https://cornell-relaxml.github.io/quip-sharp/