핵심 개념
NoMAD-Attention proposes an efficient algorithm for LLM inference on CPUs by replacing MAD operations with in-register lookups, achieving significant speedups without sacrificing model quality.
통계
LLMs exhibit emergent abilities in solving complex tasks without fine-tuning.
NoMAD-Attention achieves up to 2× speedup on 4-bit quantized LLaMA-7B-based model.
인용구
"NoMAD-Attention significantly speeds up LLM inference without sacrificing model quality."
"NoMAD-Attention leverages SIMD registers for efficient attention computations on CPUs."