Cutting LLM Memory by 84%: A Deep Dive into Fused Kernels

The article addresses the common issue of out-of-memory (OOM) errors occurring in the final layers of large language models (LLMs) during inference, which can significantly hinder performance. It introduces a solution involving the development of custom Triton kernels that fuse multiple operations, notably reducing memory usage by up to 84%, thereby enabling more efficient deployment of LLMs on hardware with limited memory capacity.