I expected a diffusion LLM to be fast on my Mac. It tied the best model on quality instead — and lost on speed.

LLaDA2.0-mini, a diffusion language model, runs on a 16 GB M3 and ties Qwen3-4B for the best answer-quality score I've measured (20/21). But it's slower than the fastest autoregressive model and uses 4× the memory of the lightest — and the exact reason I expected it to be fast on bandwidth-bound hardware turned out to be why it isn't. A measured look at where the bottleneck actually moved.