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View details ↗DISCOVER & COMPARE
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View details ↗Cosmos-1.0-Guardrail
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View details ↗Cosmos-AnomalyGen-PCB-2B
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View details ↗Cosmos-Embed1-224p
NVIDIA**Cosmos-Embed1** is a joint video-text embedder tailored for physical AI. It can be used for text-to-video retrieval, inverse video search, semantic deduplication, zero-shot and k-nearest-neighbors (kNN) classification, and as a base model for video curation tasks. It has state-of-the-art (SOTA) performance on autonomous vehicle (AV) and robotics datasets, while maintaining competitive performance in general domains. This model is ready for commercial use. The Cosmos-Embed1 release includes the following embedders: * **Cosmos-Embed1** * Cosmos-Embed1-224p (optimized with 8 frames and 224x224 input resolution, 256-dim output text and video embeddings) * Cosmos-Embed1-336p (optimized with 8 frames and 336x336 input resolution, 768-dim output text and video embeddings) * Cosmos-Embed1-448p (optimized with 8 frames and 448x448 input resolution, 768-dim output text and video embeddings) Note, while each checkpoint was optimized at a specific fixed resolution (and default to these), they all support arbitrary non-square resolutions.
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View details ↗Cosmos-Embed1-448p
NVIDIA**Cosmos-Embed1** is a joint video-text embedder tailored for physical AI. It can be used for text-to-video retrieval, inverse video search, semantic deduplication, zero-shot and k-nearest-neighbors (kNN) classification, and as a base model for video curation tasks. It has state-of-the-art (SOTA) performance on autonomous vehicle (AV) and robotics datasets, while maintaining competitive performance in general domains. This model is ready for commercial use. The Cosmos-Embed1 release includes the following embedders: * **Cosmos-Embed1** * Cosmos-Embed1-224p (optimized with 8 frames and 224x224 input resolution, 256-dim output text and video embeddings) * Cosmos-Embed1-336p (optimized with 8 frames and 336x336 input resolution, 768-dim output text and video embeddings) * Cosmos-Embed1-448p (optimized with 8 frames and 448x448 input resolution, 768-dim output text and video embeddings) Note, while each checkpoint was optimized at a specific fixed resolution (and default to these), they all support arbitrary non-square resolutions.
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View details ↗Cosmos-Guardrail1
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View details ↗Cosmos-H-Dreams
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View details ↗Cosmos-H-Surgical
NVIDIAThe base `Cosmos-H-Surgical` model remains the default when `--checkpoint-path` is omitted. The DMD2 checkpoint is an explicitly selected, student-only safetensors export; it does not contain optimizer, scheduler, teacher, fake-score, trainer, or other DCP training state. The model weights and accompanying Cosmos-H-Surgical source code are provided under OpenMDW-1.1. The official license source is available from the OpenMDW repository. Third-party software and upstream model components remain subject to their own license terms. Expected users include medical researchers, surgical robotics developers, AI developers, and healthcare institutions working on synthetic surgical video, simulation-to-real transfer, physical AI, and downstream robotics research.
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