NVIDIA Cosmos 3 で物理 AI の推論・世界モデル・行動モデルを開発する

Physical AI systems must understand the real world before they can act within it. Robots, autonomous vehicles, and smart spaces need to understand what’s happening in their world, predict what’s likely to happen next, and generate actions for specific environments, embodiments, and tasks.

NVIDIA Cosmos 3 is a frontier foundation model for physical AI that combines physical reasoning, world generation, and action generation within a single open model.

NVIDIA is open sourcing Cosmos 3 models, training scripts, deployment tools, and datasets to make physical AI development more open and reproducible. This blog post covers the fundamentals of Cosmos 3, highlights key concepts from the technical report, guides through technical workflows and shows how teamsrobotic manipulation systems, autonomous vehicles, and warehouse monitoring solutions can get started.

Key highlights of this release include:

• NVIDIA Cosmos 3 Nano and NVIDIA Cosmos 3 Super model checkpoints on Hugging Face with code on GitHub.

• Open datasets for physical AI applications like robotics and autonomous driving.

• Open post-training scripts for adapting Cosmos 3 to your domain.

• Cosmos NIM microservices for easy, optimized deployment on NVIDIA GPUs.

What’s new in Cosmos 3

Previous Cosmos releases separated world generation, physical understanding, and controlled scene generation into different models and workflows. This release unifies those capabilities with a Mixture-of-Transformers (MoT) architecture built around two towers.

• Reasoner tower: A vision-language model (VLM) that interprets multimodal observations like images, videos, and text. This tower uses an autoregressive architecture to interpret the input and understand motion, object interactions, and other physical context. This serves as the ‘brain’ that reasons about the world before any generation happens.

• Generator tower: Generates future observations and action sequences. This tower uses a diffusion-based process to generate physics-aware video and action outputs that are conditioned on the reasoner tower’s understanding. The reasoner can be called independently, but the generator always activates both towers for guided generation.

This architecture enables a single model to do reasoning and generation tasks, simplifying development by eliminating orchestration between multiple models and inference pipelines.

Choose the right model size

Two Cosmos 3 models are currently available:

• Cosmos 3 Nano is the compact version with 8B parameters and optimized for efficient inference. It’s designed to run on workstation-grade compute, like the NVIDIA RTX PRO 6000 GPU for real-time robotics inference and physical AI applications.

• Cosmos 3 Super is the 32B parameter model designed for maximum quality and capability. It delivers the highest benchmark scores and targets datacenter deployment on NVIDIA Hopper and NVIDIA Blackwell GPUs, making it suitable for large-scale synthetic data generation and advanced physical reasoning workloads.

Supported modalities

Cosmos 3 supports the following input and output modalities through its unified architecture:

Open datasets for physical AI

With the Cosmos 3 release, NVIDIA is open-sourcing six synthetic data generation (SDG) datasets on Hugging Face. These cover robotics, physics simulation, spatial reasoning, human motion, driving, and warehouse environments, and can be used for post-training Cosmos 3 and other models:

Physical AI World Model Synthetic Datasets include:

• Embodied robot scenes

• Physical interaction scenes

• Spatial reasoning

• Digital human scenes

• Autonomous driving scenarios

• Warehouse operations scenes

NVIDIA Cosmos Human Evaluation benchmark

The NVIDIA Cosmos Human Evaluation (HUE) framework assesses Cosmos 3 generator quality across representative domain tasks.

As SOTA video generation models saturate existing automated leaderboards, score differences between releases are often too narrow for meaningful comparison. HUE shifts evaluation from subjective grading to objective fact verification, enabling fine-grained comparison between top-tier models. The result is a more reliable quality signal for both rapid iteration and rigorous release decisions backed by full human evaluation.

HUE evaluates video generation quality using atomic binary verification. Each generated video is decomposed into single-fact yes/no questions across four dimensions—semantic alignment, physical laws, geometric reasoning, and visual integrity—spanning seven Physical AI domains, including robotics, autonomous vehicles, and physics. These questions are generated by a VLM pipeline, refined by human experts, and released as open source on Hugging Face.

Benchmark results

Cosmos 3 has been evaluated across multiple benchmark suites covering physical AI reasoning, generation quality, and domain-specific performance.

Reasoning benchmarks

Cosmos 3 Super and Cosmos 3 Nano lead on VANTAGE-Bench at the 32B tier and the 8B tier, respectively:

• VANTAGE-Bench: First public benchmark for evaluating vision-language models on real-world fixed-camera footage across warehouses, transportation, and smart spaces.

• Traffic Anomaly Reasoning (TAR): A new leaderboard for detecting and reasoning anomalous events in transportation footage and the official leaderboard for AI City Challenge 2026 Track 3.

Generator benchmarks

Cosmos 3 is the open-source SOTA on R-Bench and currently leads on PAIBench-G, Physics-IQ, and RoboLab across public leaderboards:

Cosmos 3 is the open-source SOTA and currently leads on PAI-Bench, R-Bench Physics-IQ, and RoboLab across public leaderboards:

• Artificial Analysis: A benchmarking platform that ranks AI models for text, image, and video generation. Cosmos 3 is the leading open source model on the Text to Image leaderboard and Image to Video (no audio) leaderboard.

• R-Bench: A benchmark for evaluating video-based world models in robotic video generation. It assesses task completion and visual quality through sub-metrics like structural consistency, physical plausibility, and execution completeness.

• PAI-Bench: A unified benchmark evaluating physical AI across video understanding and video generation, spanning domains like robotics, autonomous vehicles, and physics common sense.

• Physics-IQ: A benchmark of real-world videos that tests whether generative video models truly understand physical principles, rather than just achieving visual realism.

• RoboLab: A simulation benchmark for evaluating task-generalist robot policies.

Training recipes

A central component of the Cosmos 3 release is a fully open set of training recipes. Beyond model checkpoints, this release provides code, configs, and workflows for adapting Cosmos 3 to new domains, embodiments, and datasets.

Supervised Fine-Tuning post-training

Supervised Fine-Tuning (SFT) enables developers to adapt a Cosmos 3 model to their own data. The released recipes include vision generation post-training for custom video datasets, as well as action-oriented recipes for robotics and physical AI workflows. Developers can customize Cosmos 3 for their target domains across robotics, autonomous driving, and warehouse automation.

The post-training code and configs are available on GitHub.

Action post-training

Action post-training adapts Cosmos 3 for action-aware Physical AI applications, including forward dynamics, inverse dynamics, and policy generation. Developers can post-train Cosmos 3 on action-labeled data. For robotics applications, this includes several important workflows: generating future observations conditioned on robot actions, inferring the actions behind observed demonstrations, and predicting action sequences from current observations and task prompts. This makes Cosmos 3 a strong foundation for world action modeling and policy learning.

Deploy with NVIDIA NIM Microservices

Cosmos 3 models are also available as NVIDIA NIM microservices for optimized, production-ready deployment. NIM microservices package the model with optimized inference runtimes, delivering high performance without the need to manually tune serving infrastructure. NIM microservices are easier to use for inference workflows compared to the Cosmos 3 repo on GitHub, which is preferred for post-training workflows.

The Cosmos 3 Reasoner NIM is available today, delivering the reasoning capabilities of the Cosmos 3 model. Keep posted for the Cosmos 3 Generator NIM, which provides full generation capabilities of the Cosmos 3 model.

Optimizations made to accelerate inference

• Quantization: Cosmos 3 NIM supports selecting BF16, FP8, or NVFP4 quantized checkpoints. The NVFP4 quantization reduces the model’s numerical precision from BF16 to 4-bit floating point, achieving up to 2x inference speedup.

• vLLM: Is an open source inference engine that uses techniques like continuous batching, paged attention, and tensor parallelism to serve LLMs efficiently. The Cosmos 3 Reasoner NIM serving stack is built on vLLM for higher throughput compared to conventional serving approaches. Cosmos 3 Nano is ready to run with vLLM-omni and NVIDIA Dynamo for top performance.

• Efficient Video Sampling (EVS): This technique reduces the number of video tokens fed into the VLM during inference, speeding up the Cosmos Reason NIM. EVS works at the chunk level, keeping the most unique chunks of each frame and pruning the rest. Smaller GPUs tend to benefit more from this technique.

How to run the NIM

An NVIDIA NGC API key is required to pull the containers and download the Cosmos 3 models from NGC.

To pull and run the Cosmos3 Nano Reasoner NIM. For the Cosmos3 Super Reasoner NIM, specify NIM_MODEL_SIZE=super.

docker run --gpus=all \

-e NGC_API_KEY=$NGC_API_KEY \

-e NIM_MODEL_SIZE=nano \

-p 8000:8000 \

nvcr.io/nim/nvidia/cosmos3-reasoner:latest

Find details on API usage and more in the documentation.

Get started

• Download the Cosmos 3 Nano and Super checkpoints on Hugging Face.

• Find examples and code on the Cosmos 3 GitHub.

• Try the Cosmos 3 Nano Reasoner model experience and the Cosmos 3 Nano model experience.

• Join the community, open issues, and contribute to the Cosmos ecosystem on GitHub and Discord.

*Acknowledgments*

*Cosmos 3 is the result of amazing collaboration between many teams and people across NVIDIA, including Adeline Aubame, Aditya Mahajan, Aigul Dzhumamuratova, Akash Gokul, Akul Santhosh, Aleksandr Efitorov, Alex Sotelo, Alexander Schwarz, Alperen Degirmenci, Amol Fasale, Andrew Tham, Ankur Handa, Arihant Jain, Arslan Ali, Artur Zolkowski, Aryaman Gupta, Asawaree Bhide, Ashkan Mirzaei, Ashley Chow, Ashna Khetan, Atharva Joshi, Barnaby Simkin, Benedikt Falk, Brett Hamilton, Carlos Casanova, Chaeyeon Chung, Charles Zhou, Chen-Hsan Lin, Chen-Hsuan Lin, Chhavi Nijhawan, Chieh-Yun Chen, Chintan Shah, Chris Helvig, Chris Pruett, Cindy Zha, Cyrus Hogg, Dahjung Chung, Dan Blick, David Wehr, Dawid Majchrowski, DeLesley Hutchins, Delin Qu, Dennis Lynch, Diego Garzon, Dima Zhylko, Durra Mohsin, Egor Krivov, Ekram Mukbil, Eric Cameracci, Fangyin Wei, Fengzhe Zhou, Francesco Ferroni, Freya Li, George Kurian, Gwanghyun Kim, Haaland Hao Liang, Hai Loc Lu, Hans Yang, Hao Liang, Hao Wang, Hesam Rabeti, Hugo Hadfield, Hyejin Moon, Itai Zadok, Jayjun Lee, Jeana Choi, JF Lafleche, Jiangran Lyu, Jiaojiao Fan, Jiaxiang Tang, Jibin Varghese, Jim Fan, Jingyi Jin, Jinwei Gu, Jon Allen, Joshua Bapst, Joyjit Daw, Julia Kiczka, Julian Ouyang, Kaichun Mo, Kayley Ting, Ke Ding, Kedi Wu, Kevin Brady, Kirill Motkov, Kristen Rumley, Krzysztof Tomala, Liang Feng, Liangkai Zhang, Ling Li, Louis Marcoux, Maciej Bala, Madison Huang, Magdalena Dadela, Mahesh Patekar, Marco Di Lucca, Marilyn Reeb, Mark Carlson, Martin Antolini, Mateusz Sieniawski, Matt Cragun, Meredith Price, Michael Huang, Miguel Guerrero, Miguel Martin, Min Shi, Ming-Yu Liu, Mohammad Harrim, Morteza Ramezanali, Mukesh Beladiya, Nalin Dadhich, Naomi Eigbe, Nathan Hayes-Roth, Nicole Drumheller, Nikhilesh Joshi, Omar Laymoun, Paris Zhang, Paula Ramos, Pawel Morkisz, Peter Gambrill, Pooya Jannaty, Pooya Khaloo, Pranjali Joshi, Qi Wang, Qianli Ma, Qiao Wang, Qing Miao, Qizhi Chen, Rahul Heinrich Steiger, Raju Wagwani, Robert Denomme, Rodrigo Vieira Del Monte, Roy Anthony, Ruqing Xu, Ryan Bernard, Ryan Ji, Saeid Motiian, Sandip Bhaskar, Sandra Skaff, Santanu Dutta, Saurav Kumar, Sehwi Park, Sergiy Fefilatyev, Shangkun Sun, Shangru Li, Shilin Zhu, Shreyas Misra, Shun Zhang, Shuran Song, Simon Yuen, Simon Zhang, Slawek Kierat, Smita Ithape, Soha Pouya, Sophia Huang, Stefanie Manzinger, Steven Baughman, Suneel Indupuru, Sunil Srinivasa, Sunny Kim, Tavish Chen, Thabang Ngazimbi, Thomas Volk, Tianwei She, Tiffany Cai, Ting-Chun Wang, TJ Galda, Tolou Tavakkoli, Tomasz Kornuta, Trung Pham, Tsung-Yi Lin, Vanni Brighella, Varun Praveen, Wei-Cheng Tseng, Wenjie Luo, Wesley Li, Wojciech Kutak, Wojciech Rymer, Xiangyu Lu, Xiaodong Yang, Xiaotong Chen, Xin Kong, Xinquan Xu, Xiu Chia, Xuning Yang, Yan Chang, Yan Wang, Yanan Jian, Yao Xu, Yashraj Narang, Yeongho Seol, Yichu Yang, Yifan Ding, Yihuai Gao, Yilin Zhao, Yin Cui, Yogesh Balaji, Yu Wang, Yu-Wei Chao, Yue Tang, Yufan Huang, Yuke Zhu, Yuliya Zhautouskaya, Yurong You, Yuzhu Dong, Zaid Pervaiz Bhat, Zekun Hao, Zhaoshuo Li, Zhizheng Zhang.

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