NVIDIA's Cosmos 3 Super Cuts Video Generation From 50 Steps to 4
NVIDIA's 4-step distilled Cosmos 3 Super models hit #1 open-weights image-to-video and #3 text-to-image on the Artificial Analysis leaderboard, with up to 25x faster inference.
- New #1 open-weights I2V: Cosmos3-Super-Image2Video-4Step tops the Artificial Analysis leaderboard, beating its own 35-step teacher model.
- 25x faster inference: DMD2 distillation cuts denoising from 50 steps to 4 and removes classifier-free guidance entirely.
- Open weights + commercial use: Both models are on Hugging Face under the OpenMDW-1.1 license, free to use commercially.
- Distillation recipe included: NVIDIA open-sourced the full DMD2 training scripts so teams can distill their own fine-tuned Cosmos3 checkpoints.
- Physical AI focus: Designed for robotics simulation, autonomous driving SDG, and warehouse safety data generation -- not primarily a creative tool.
- Known failure modes: Temporal inconsistency, object morphing, and implausible physics remain common issues in complex or long outputs.
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.