Diffusion Models Will Break Transformer Sequential Inference Ceiling, Says Stanford Researcher
StanfordAILab · x · 2026-08-11
Stanford researcher Aditya Grover argued at the AI4 conference that parallel inference is inevitable. Drawing parallels to how GPUs parallelized matrix multiplication and Transformers parallelized training, he stated that sequential token generation has a ceiling, and diffusion models will break it by parallelizing inference.
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