Kernel fusion for efficient LLM training and inference
Which kernel-fusion methods improve the efficiency of large language model training and inference, and what gains do they deliver?
Kernel fusion — computing several operations in one GPU kernel to cut memory traffic and launch overhead — is the dominant mechanism behind the efficiency gains of the past six years. The evidence is consistent that attention kernels (FlashAttention and its descendants) deliver 2–4x speedups and near-constant memory in sequence length, that fused training-op kernels (Liger and peers) recover roughly 20% throughput and 60% memory on the training side, and that FP8 pipelines now train 10–75% faster with matched quality. Most evidence is preprint-grade and hardware-specific; the field lacks a shared benchmark, and the newest frontier is using LLMs themselves to write the kernels.
Updated 8 Aug 202692 sources2019–2026Deep17 min read
kernel fusion · GPU kernels · FlashAttention · LLM training · LLM inference · Triton · FP8 · Mixture-of-Experts