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Inference & ServingMedium· Diffusion Models· Serving Constraints
If diffusion models are so much faster, why isn’t every...
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See plansWhile diffusion models offer high throughput, they are not currently a universal replacement for AR models. The quality gap in instruction following and factual recall is significant. Furthermore, the requirement to specify output length upfront introduces serving complexity and potential padding waste. The compute advantage is only realized when the output length is long enough to amortize the per-step weight-sweep cost, making them better suited for high-throughput batch workloads than interactive chat applications.