The AI Memory Wall: Why LLMs & AI Accelerators Hit a Bottleneck

A walkthrough of the memory wall, explaining why raw chip compute growth has not translated into proportional AI workload speedups. It covers arithmetic intensity, the roofline model and ridge points for the B200 GPU, why LLM decode is memory-bound while prefill is compute-bound, and how batching and continuous batching shift workloads toward higher efficiency.