
NVIDIA Cosmos
World Foundation Models for Physical AI
One model family that sees, reasons, simulates, and acts.
Try it in your browser · Quickstart · Find your path · Model Family

[Physics-aware generation](cookbooks/cosmos3/generator/audiovisual/run_with_vllm_omni.ipynb)
A Newton's cradle in motion: momentum transfer rendered with physical fidelity

[Transfer](cookbooks/cosmos3/generator/transfer/run_video_transfer_with_vllm_omni.ipynb)
World-scenario control layout → photoreal driving video

[Action policy](cookbooks/cosmos3/generator/action/run_policy_with_vllm_omni.ipynb)
Policy run: "put the screwdriver and the glove in the purple container"

[Simulation](cookbooks/cosmos3/generator/audiovisual/run_with_vllm_omni.ipynb)
Generate synthetic data and create simulation for autonomous driving

[Action-conditioned World Model](cookbooks/cosmos3/generator/action/run_fd_with_vllm_omni.ipynb)
Forward dynamics: egocentric rollout from input camera + hand pose

[World reasoning](cookbooks/cosmos3/reasoner/run_with_vllm.ipynb)
Reason in complex real-world scenarios: a rolling ball means a child or pet may follow
What's new
- [Jul 2026] Cosmos3-Edge released — the 4B tier for on-device, real-time deployment (Jetson AGX Orin / Thor / RTX Pro 6000).
- [May 2026] Cosmos 3 released: HF collection · Technical Report.
What is Cosmos?
NVIDIA Cosmos is an open platform for building physical AI applications — robots, autonomous vehicles, and smart infrastructure — providing better data, better environment, better starting point, and better tooling for physical AI developers. Cosmos 3, the current model family, is a suite of omnimodal world models built on a unified Mixture-of-Transformers architecture (technical report).
One model, two surfaces:
| Inputs | Outputs | Use it for | |
|---|---|---|---|
| Reasoner | text, vision | text | world understanding, grounding, task planning, embodied reasoning |
| Generator | text, vision, sound, action | vision, sound, action | world simulation, future prediction, synthetic data, policy learning |
This repository is the home of the models: everything for exploring, running, and evaluating Cosmos. For model training (SFT, LoRA, RL, distillation etc.), go to Cosmos Framework. Use Cosmos Curator for data curation, and Cosmos Evaluator for model output evaluation.
Find your path
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Time
See it work — zero install
Video generation, visual reasoning
1 min
Generate my first video
10 min
Reason over images & video
10 min
Serve an OpenAI-compatible API
Serving setup guide — vLLM, vLLM-Omni, or NIM
30 min
Post-train on my own data — SFT, distillation, RL
Cosmos Framework, then evaluate here
hours
Explore runnable notebooks
browse
Evaluate a model
Evaluation suites — PAIBench, Physics-IQ, VLMEvalKit
hours
Check latency & throughput
browse
Generate your first video
Before running the code, request access to nvidia/Cosmos-1.0-Guardrail and accept its access conditions. Once access is granted, log in below with a Hugging Face read token from the same account. Logging in alone does not grant access.
uv venv --python 3.13 --seed --managed-python && source .venv/bin/activate
uv pip install --torch-backend=auto \
"diffusers @ git+https://github.com/huggingface/diffusers.git" \
accelerate av cosmos_guardrail huggingface_hub imageio imageio-ffmpeg \
torch torchvision transformers
uvx hf@latest auth login # Authenticate for the gated Guardrail repository
import torch
from diffusers import Cosmos3OmniPipeline
from diffusers.utils import export_to_video
pipe = Cosmos3OmniPipeline.from_pretrained(
"nvidia/Cosmos3-Nano", torch_dtype=torch.bfloat16, device_map="cuda"
)
video = pipe(prompt="A mobile robot navigates a warehouse aisle and stops at a shelf.").video
export_to_video(video, "first_video.mp4", fps=24)
First run downloads the 16B checkpoint; diffusion steps are compute-heavy, so long step times are normal. Full options, image/sound modes, and every other backend: audiovisual cookbooks · setup issues: environment setup guide.
Models
Cosmos 3 ships as three base models — every deployment tier, one omnimodal architecture:
| Base model | Size | Runs on | Best for |
|---|---|---|---|
| Cosmos3-Super | 64B | H200 / B200 / GB200 | Highest quality; synthetic data generation; teacher for distillation |
| Cosmos3-Nano | 16B | RTX Pro 6000 / H100 / B200 | Balanced speed and quality; strong base model to post-train |
| Cosmos3-Edge | 4B | Jetson AGX Orin / Thor / RTX Pro 6000 | Edge deployment; real-time robot policy and visual reasoning |
Example checkpoints in the cosmos3-examples collection are post-trained variants of the base models. They demonstrate what post-training with Cosmos Framework can specialize Cosmos for — they're capability demos, not part of the product line:
| Example checkpoint | Base | Demonstrates |
|---|---|---|
| Cosmos3-Super-Text2Image | Super | Elite quality text-to-image |
| Cosmos3-Super-Text2Image-4Step | Super | Elite quality text-to-image, 17-25x faster |
| Cosmos3-Super-Image2Video | Super | Elite quality image-to-video |
| Cosmos3-Super-Image2Video-4Step | Super | Elite quality image-to-video, 17-25x faster |
| Cosmos3-Nano-Policy-DROID | Nano | Open SOTA DROID robot policy, runs on RTX Pro 6000 |
| Cosmos3-Edge-Policy-DROID | Edge | DROID robot policy at edge-deployable scale |
Full I/O specs, generation settings, and supported action embodiments: model reference.
Repository map
cosmos/
├── cookbooks/ # runnable notebooks for every capability (start here to explore)
│ └── cosmos3/ # generator (audiovisual · action · transfer) · reasoner + prompt guide
├── evaluation/ # quality benchmark suites: PAIBench, Physics-IQ, RBench, UniGenBench, VLMEvalKit
├── docs/
│ └── reference/ # lookup: model reference
├── assets/ # brand + demo media
└── README.md # you are here
Training, optimization, and deployment tooling lives in Cosmos Framework.
Platform
| Project | Purpose |
|---|---|
| Cosmos | This repo |
| Cosmos Framework | Train, optimize, and deploy physical AI models — SFT · LoRA · distillation · RL post-training, for Cosmos and beyond |
| Cosmos Curator | Distributed data curation: processing, annotation, filtering, dedup |
| Cosmos Evaluator | Automated evaluation system for world generation & reasoning outputs |
Cosmos 3 runs on Diffusers, Transformers, vLLM, vLLM-Omni, SGLang, TensorRT-LLM, and NIM — pick a backend in the environment setup guide.
Community & contributing
Questions and ideas → Discussions. Bugs → Issues. Code → CONTRIBUTING.md.
Limitations & safety
Cosmos 3 can produce artifacts in long, high-resolution, or physically complex outputs (temporal inconsistency, object morphing, implausible dynamics). Safety-critical applications need additional validation and system-level safety analysis. Generation ships with guardrails on by default.
Citation & license
@techreport{nvidia2026cosmos3,
title = {Cosmos 3: Omnimodal World Models for physical AI},
author = {{NVIDIA Cosmos Team}},
year = {2026},
url = {https://research.nvidia.com/labs/cosmos-lab/cosmos3/technical-report.pdf}
}
Source code and models are released under OpenMDW-1.1. Custom licensing: . This project may download third-party open source software; review those licenses before use.