LLM-MQ: Mixed-precision Quantization for Efficient LLM Deployment

Large Language Models (LLMs) have demonstrated impressive performance across various tasks. Nevertheless, deploying LLMs on edge devices presents significant challenges, primarily due to their substantial model size (e.g., over 10 billion parameters). Low-precision quantization is a promising way to reduce the memory requirement of LLMs. However, directly applying ultra-low-bit quantization to LLMs leads to significant performance degradation and fails to meet a specific weight memory budget. In this paper, we propose LLM-MQ, a Mixed-precision Quantization method, to address the above issues. Our method mainly contains three folds: (1) We propose a sparse outlier protection strategy for low-precision layers by protecting the outliers in FP16 format to maintain the performance. (2) We propose sensitivity-based precision allocation to assign the proper bit-width for each layer within the given budget for weight memory based on their first-order information and quantization error. (3) We develop efficient CUDA core kernels to accelerate mix-precision LLMs by fusing the dequantization and General Matrix-Vector Multiplication (GEMV). With comparable performance on various tasks, LLM-MQ can flexibly quantize LLMs that meet the given budget for weight memory. On NVIDIA T4 GPU, we achieve up to 1.6× end-to-end speedup compared to the pytorch FP16 baseline.