QUICK: Quantization-aware Interleaving and Conflict-free Kernel for
  efficient LLM inference
Taesu Kim Jongho Lee Daehyun Ahn Sarang Kim Jiwoong Choi Minkyu Kim Hyungjun Kim
We introduce QUICK, a group of novel optimized CUDA kernels for the efficient
inference of quantized Large Language Models (LLMs). QUICK addresses the shared
memory bank-conflict problem of state-of-the-art mixed precision matrix
multiplication kernels. Our method interleaves the quantized weight matrices of
LLMs offline to skip the shared memory write-back after the dequantization. We
demonstrate up to 1.91x speedup over existing kernels of AutoAWQ on larger
batches and up to 1.94x throughput gain on representative LLM models on various
NVIDIA GPU devices.