NVIDIA just released distilled versions of its 64B Cosmos 3 Super world foundation model, and the benchmarks are turning heads. Cosmos3-Super-Image2Video-4Step and Cosmos3-Super-Text2Image-4Step are now the top-ranked open-weights models in their respective categories on the Artificial Analysis Arena, beating out the full-step teacher models they were distilled from.

50 steps down to 4

The headline number is the step reduction. The distilled models cut sampling from 50 denoising steps to just 4, and remove the need for classifier-free guidance (CFG) -- which otherwise doubles the work per step -- for up to 25x faster inference than the base model, with little to no loss in generation quality. CFG is a technique that runs the model twice per step (once with the prompt, once without) to steer outputs toward the text description; removing it is a significant efficiency win on its own.

The technique behind this is called DMD2 (Improved Distribution Matching Distillation). DMD formulates diffusion distillation as a distribution-level matching problem, training a student generator to match the teacher distribution rather than explicitly imitating teacher sampling trajectories. Its objective combines a distribution matching term with a regression loss for training stability. DMD2 further improves this by eliminating the costly regression dataset, adopting a two-time-scale update for more accurate score estimation, and incorporating adversarial supervision. In plain terms: instead of forcing the student model to retrace the teacher's exact denoising path, DMD2 just asks it to produce outputs that look statistically indistinguishable from the teacher's -- a much looser and more efficient constraint.