**Model Collection** | **Code** | **White Paper** | **Website** Cosmos3 is a collection of Omnimodal world models capable of generating dynamic, high-quality video, image, audio, and action commands from combinations of text, image, video, and action trajectory inputs. It serves as a foundational building block for a broad range of Physical AI applications and research spanning world understanding, world generation, simulation, and embodied policy learning. - Cosmos3-Super: - Given multimodal inputs including text, images, video, audio, and action trajectories, generate coherent text, images, video, audio, and action outputs for multimodal understanding, world simulation, future prediction, action reasoning, and Physical AI applications.
**Model Collection** | **Code** | **White Paper** | **Website** The highlighted checkpoint, Cosmos3-Super-Image2Video, is a post-trained image-to-video variant of the Cosmos3-Super base model. Cosmos3 is a collection of Omnimodal world models capable of generating dynamic, high-quality video, image, audio, and action commands from combinations of text, image, video, and action trajectory inputs. It serves as a foundational building block for a broad range of Physical AI applications and research spanning world understanding, world generation, simulation, and embodied policy learning.
NVIDIA Cosmos™ is a world foundation model platform designed to accelerate the development of Physical AI by enabling machines to understand, simulate, and interact with the physical world across robotics, autonomous driving, and smart space environments, including industrial and factory-scale applications. The highlighted checkpoint, Cosmos3-Super-Text2Image, is a post-trained text-to-image variant of the Cosmos3-Super base model. Cosmos3 is a collection of Omnimodal world models capable of generating dynamic, high-quality video, image, audio, and action commands from combinations of text, image, video, and action trajectory inputs. It serves as a foundational building block for a broad range of Physical AI applications and research spanning world understanding, world generation, simulation, and embodied policy learning.
Describe Anything Model 3B (DAM-3B) takes inputs of user-specified regions in the form of points/boxes/scribbles/masks within images, and generates detailed localized descriptions of images. DAM integrates full-image context with fine-grained local details using a novel focal prompt and a localized vision backbone enhanced with gated cross-attention. The model is for research and development only. This model is ready for non-commercial use. This model is intended to demonstrate and facilitate the understanding and usage of the describe anything models. It should primarily be used for research and non-commercial purposes. This model was developed based on VILA-1.5. This model has 3B of model parameters.
Long Lian, Yifan Ding, Yunhao Ge, Sifei Liu, Hanzi Mao, Boyi Li, Marco Pavone, Ming-Yu Liu, Trevor Darrell, Adam Yala, Yin Cui Describe Anything Model 3B Video (DAM-3B-Video) takes inputs of user-specified regions in the form of points/boxes/scribbles/masks within images/videos, and generates detailed localized descriptions of images/videos. DAM integrates full-image/video context with fine-grained local details using a novel focal prompt and a localized vision backbone enhanced with gated cross-attention. The model is for research and development only. This model is ready for non-commercial use. This model is intended to demonstrate and facilitate the understanding and usage of the describe anything models. It should primarily be used for research and non-commercial purposes.
The NVIDIA DeepSeek-R1-0528-FP4 v2 model is the quantized version of the DeepSeek AI's DeepSeek R1 0528 model, which is an auto-regressive language model that uses an optimized transformer architecture. For more information, please check here. The NVIDIA DeepSeek R1 FP4 model is quantized with TensorRT Model Optimizer. Compared to nvidia/DeepSeek-R1-0528-FP4, this checkpoint additionally quantizes the wo module in attention layers. This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party’s requirements for this application and use case; see link to Non-NVIDIA (DeepSeek R1) Model Card.
The NVIDIA DeepSeek V3-0324 FP4 model is the quantized version of DeepSeek AI's DeepSeek V3-0324 model, which is an auto-regressive language model that uses an optimized transformer architecture. For more information, please check here. The NVIDIA DeepSeek V3-0324 FP4 model is quantized with TensorRT Model Optimizer. This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party’s requirements for this application and use case; see link to Non-NVIDIA (DeepSeek V3-0324) Model Card. Developers looking to take off the shelf pre-quantized models for deployment in AI Agent systems, chatbots, RAG systems, and other AI-powered applications.
The NVIDIA DeepSeek-V4.1-Flash NVFP4 model is the quantized version of DeepSeek AI's DeepSeek-V4.1-Flash model, which is an auto-regressive language model that uses an optimized transformer architecture. DeepSeek-V4.1-Flash is a natively multimodal Mixture-of-Experts (MoE) model with a Causal Encoder-Decoder (CED) architecture, Compressed Sparse Attention 2 (CSA2), and support for contexts of up to one million tokens. For more information, please check here. The NVIDIA DeepSeek-V4.1-Flash NVFP4 model is quantized with Model Optimizer. This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party's requirements for this application and use case; see link to Non-NVIDIA (DeepSeek-V4.1-Flash) Model Card from DeepSeek AI. Developers looking to take off-the-shelf, pre-quantized models for deployment in AI Agent systems, chatbots, RAG systems, and other AI-powered applications.
The NVIDIA DeepSeek-V4-Flash-0731-NVFP4 model is the quantized version of DeepSeek AI's DeepSeek-V4-Flash-0731 model, an autoregressive Mixture-of-Experts language model that uses an optimized Transformer architecture with hybrid attention (Compressed Sparse Attention and Heavily Compressed Attention) and Manifold-Constrained Hyper-Connections. DeepSeek-V4-Flash-0731 is the official DeepSeek-V4-Flash release and includes DeepSeek's DSpark speculative decoding module in the same checkpoint. For more information, refer to the DeepSeek-V4-Flash-0731 model card. The NVIDIA DeepSeek-V4-Flash-0731-NVFP4 model is quantized with Model Optimizer. This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party’s requirements for this application and use case; see link to Non-NVIDIA (DeepSeek-V4-Flash-0731) Model Card. DeepSeek V4 is well-suited for advanced reasoning, agentic AI applications, tool use scenarios, and complex problem-solving in domains such as mathematics, software engineering, and enterprise AI assistants.
The NVIDIA DeepSeek-V4-Flash-NVFP4 model is a quantized version of DeepSeek AI's DeepSeek-V4-Flash model, an autoregressive Mixture-of-Experts language model that uses an optimized Transformer architecture with hybrid attention (Compressed Sparse Attention and Heavily Compressed Attention) and Manifold-Constrained Hyper-Connections. For more information, refer to the DeepSeek-V4-Flash model card. The NVIDIA DeepSeek-V4-Flash-NVFP4 model is quantized with Model Optimizer. This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party’s requirements for this application and use case; see link to Non-NVIDIA (DeepSeek-V4-Flash) Model Card. DeepSeek V4 is well-suited for advanced reasoning, agentic AI applications, tool use scenarios, and complex problem-solving in domains such as mathematics, software engineering, and enterprise AI assistants.
