2024
Fast Matrix Multiplications for Lookup Table-Quantized LLMs
EMNLP 2024finding
The deployment of large language models (LLMs) is often constrained by memory bandwidth, where the primary bottleneck is the cost of transferring model parameters from the GPU’s global memory to its registers. When coupled with custom kernels that fuse the dequantization and matmul operations, weigh…