NVIDIA recently released SANA-WM, a 2.6B-parameter open-source world model capable of generating one-minute-long 720p-resolution video on a single GPU, with metric-scale 6-DoF camera control. The model is publicly available on arXiv (arXiv:2605.15178), and code and weights are accessible in the NVlabs/Sana GitHub repository.
The architectural core is Hybrid Linear Attention: most attention blocks are replaced by frame-level Gated DeltaNet (GDN), introducing a decay gate γ to address long-video state drift, keeping the recurrent state at constant dimension.
Two-stage pipeline: the first stage generates low-resolution rough output, and the second stage improves quality through a long-video refiner. The 4-step distilled version completes 60 seconds of 720p video denoising in just 34 seconds on a single RTX 5090 (NVFP4 quantized), with throughput 36× that of previous open-source solutions. Training requires only about 213,000 public video segments, completed in 15 days on 64 H100s.
SANA-WM's significance lies in making both the training and inference of world models achievable on limited compute. When minute-level, 720p, camera-controlled video generation can run on consumer-grade hardware, the practical value of world models as a data source for robot simulation and embodied-intelligence training truly opens up. From "can run" to "affordable to use," this is the most pragmatic step for the world-model field in 2026.