Cosmos 3 — NVIDIA 的本地 AI 模型

作者： Jakub Rusinowski · 最后更新： 2026年7月21日

PREVIEW (2026). NVIDIA's open frontier World Foundation Model (WFM) for Physical AI — the first fully open omnimodel that natively understands and generates text, images, video, ambient audio and robot/vehicle actions in a single system, with leading physics accuracy. Cosmos 3 is a two-tower Mixture-of-Transformers (MoT): a reasoner tower (an autoregressive vision-language "brain" that interprets images/video/text and reasons about motion, object interactions and physical cause-and-effect) plus a diffusion-based generator tower that predicts physics-aware future video and action sequences conditioned on that reasoning — so it "thinks before it acts." The two towers keep separate norm layers and MLPs but share multimodal attention. Trained on ~20 trillion tokens of multimodal data (nearly a billion images, 400M+ real and synthetic videos, plus audio and human/robot action data), it compresses physical-AI training and evaluation from months to days by generating synthetic worlds and predicting actions for robots and autonomous vehicles. Ships in three scales — Super (64B), Nano (16B) and Edge (4B) — under the permissive OpenMDW-1.1 license, on Hugging Face (nvidia/) and NVIDIA NGC. NVIDIA reports open-source state-of-the-art results on R-Bench, PAI-Bench, Physics-IQ and RoboLab; treat launch numbers as vendor-reported pending independent replication. This is a robotics / autonomous-vehicle / world-simulation model, not a chat LLM — verify specs on the model cards before relying on them.