🤖 AI Summary
Existing post-training quantization methods for large language models struggle to achieve efficient sub-1-bit compression, often hindered by high data or computational demands and additional storage overhead. This work proposes NanoQuant, the first post-training quantization framework capable of both binarization and sub-1-bit compression. By integrating low-rank binary matrix decomposition, ADMM-based initialization, and block-wise reconstruction, NanoQuant compresses Llama2-70B by 25.8× (averaging <1 bit per parameter) in under 13 hours on a single H100 GPU, enabling its deployment on an 8GB consumer-grade GPU. This dramatically lowers the hardware barrier for inference while establishing a new Pareto frontier between model accuracy and compression ratio.
📝 Abstract
Weight-only quantization has become a standard approach for efficiently serving large language models (LLMs). However, existing methods fail to efficiently compress models to binary (1-bit) levels, as they either require large amounts of data and compute or incur additional storage. In this work, we propose NanoQuant, the first post-training quantization (PTQ) method to compress LLMs to both binary and sub-1-bit levels. NanoQuant formulates quantization as a low-rank binary factorization problem, and compresses full-precision weights to low-rank binary matrices and scales. Specifically, it utilizes an efficient alternating direction method of multipliers (ADMM) method to precisely initialize latent binary matrices and scales, and then tune the initialized parameters through a block and model reconstruction process. Consequently, NanoQuant establishes a new Pareto frontier in low-memory post-training quantization, achieving state-of-the-art accuracy even at sub-1-bit compression rates. NanoQuant makes large-scale deployment feasible on consumer hardware. For example, it compresses Llama2-70B by 25.8$\times$ in just 13 hours on a single H100, enabling a 70B model to operate on a consumer 8 GB GPU.