Making AI Faster Is a Logistics Problem

The author explains that language model inference speed is primarily a memory bandwidth problem rather than a compute problem, as GPUs must read all model weights from memory for every generated token. The article outlines how to measure performance against physical speed limits, describes parallelization strategies, and proposes a prioritized sequence for optimizing inference servers. Open article →