Introducing NVIDIA Cosmos 3: Unified Multimodal Model for Physical AI
TL;DR · AI Summary
NVIDIA launches Cosmos 3, the first unified multimodal model integrating language, video, sound, and action inputs/outputs, built on Mixture of Transformer architecture, open-sourced with weights available on Hugging Face, achieving top scores across physical AI benchmarks including Robo Lab, PiBench, and Vintage.
Key Takeaways
- Cosmos 3 is the first omni-model combining language, video, audio, and action mo
- The Super version delivers state-of-the-art accuracy in physical AI tasks; Nano
- Cosmos 3 ranks #1 in Robo Lab policy evaluation, PiBench, Vintage, and TA benchm
Outline
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NVIDIA introduces Cosmos 3 to accelerate the physical AI revolution by providing a unified foundation model for customization and deployment.
Built on Mixture of Transformer with dual towers — autoregressive left and diffusion right — supporting vision-language-action models.
Two variants: Super (high accuracy) and Nano (lightweight for edge), weights available via Hugging Face, code on GitHub.
Top-ranked across physical AI benchmarks including Robo Lab, PiBench, Vintage, and TA; first in open-source image-to-video generation.
Provides training scripts and datasets to empower developers to build downstream applications using the open model.
Mindmap
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- NVIDIA Cosmos 3：统一物理AI多模态模型
- 核心架构
- Mixture of Transformer
- 双塔设计：自回归 + 扩散
- 版本策略
- Super 模型：高精度物理AI任务
- Nano 模型：边缘设备部署
- 性能表现
- Robo Lab 政策评估第一
- PiBench / Vintage / TA 基准榜首
- 开源图像到视频生成第一
- 开源生态
- Hugging Face 开源权重
- GitHub 示例代码与训练脚本
Highlights
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Cosmos 3 is the first omni-model integrating language, video, sound, and action inputs/outputs, leveraging a novel Mixture of Transformer architecture combining autoregressive and diffusion mechanisms
Ranked #1 in Robo Lab policy evaluation and multiple physical AI benchmarks including PiBench, Vintage, and TA, demonstrating superior physical reasoning and generation capabilities.
NVIDIA offers Super and Nano versions — Super for high-performance AI tasks, Nano for edge devices — lowering deployment barriers for developers.