Cosmos

Website | Framework

Table of Contents

• Introduction
• Cosmos 3
  • Key Capabilities
  • Model Architecture
  • Model Family
  • Supported Generation Settings
  • Input and Output
  • Use Cases
    • Generator
    • Reasoner
  • Quickstart
    • Generator with Diffusers
    • Generator with vLLM-Omni
    • Reasoner with Transformers
    • Reasoner with vLLM
  • Troubleshooting
    • Which CUDA version should I use?
    • Which base container should I use?
    • torch.cuda.is_available() is False
    • Import fails with libxcb.so.1: cannot open shared object file
    • uv errors on install or sync
  • Choosing an Integration
  • Examples
  • Inference Benchmarks
  • Limitations
• Ecosystem
• News
• License and Contact

Introduction

NVIDIA Cosmos is an open platform of world models, datasets, and tools that enables developers to build Physical AI for robots, autonomous vehicles, smart infrastructure, and more.

Cosmos 3

Cosmos 3 is our newest model family [Cosmos 3 Technical Report]. It is a suite of omnimodal world models designed to jointly process and generate language, images, video, audio, and action sequences within a unified Mixture-of-Transformers architecture. By supporting highly flexible input-output configurations, it seamlessly unifies critical modalities for Physical AI — effectively subsuming vision-language models, video generators, world simulators, and world-action models into a single framework.

Cosmos 3 exposes two runtime surfaces:

Surface	Inputs	Outputs	Use Cases
Reasoner	Text, vision	Text	World understanding, grounding, physical reasoning, task planning, action forecasting, embodied agent reasoning, and autonomous system decision making
Generator	Text, vision, sound, action	Vision, sound, action	World generation, world simulation, future prediction, synthetic data generation, policy learning, and robot training

Key Capabilities

• World understanding: Analyze videos and images for captions, temporal events, next actions, spatial grounding, physical plausibility, and causal outcomes.
• World generation: Produce images, videos, synchronized sound, and action-conditioned rollouts from text, image, video, or action inputs.
• Action modeling: Predict policy actions, inverse dynamics, and forward dynamics for robotics, camera motion, egocentric motion, and autonomous-driving settings.
• Research and production paths: Use Diffusers and Transformers for Python-first development, then vLLM-Omni and vLLM for OpenAI-compatible serving.
• Post-training recipes: Adapt vision, action, and reasoner workflows with Cosmos Framework training recipes and task-specific evaluation [Coming Soon].

Model Architecture

Cosmos 3 is an omnimodal world model built on a unified Mixture-of-Transformers (MoT) architecture that combines an autoregressive (AR) transformer for reasoning with a diffusion transformer (DM) for multimodal generation. In Reasoner Mode, language and visual understanding tokens are processed through causal self-attention, enabling next-token prediction for tasks such as perception, planning, and world reasoning. In Generator Mode, noisy image, video, audio, and action tokens are denoised through full attention, allowing the model to jointly generate coherent multimodal outputs. Both modes share the same transformer architecture, multimodal attention layers, and a unified 3D multi-dimensional rotary position embedding (mRoPE) representation that encodes spatial and temporal structure across modalities, enabling consistent reasoning over images, videos, audio streams, and action trajectories.

Model Family

Model	Size	Primary Capability
Cosmos3-Nano	16B	Compact omnimodal world model for multimodal understanding, world simulation, future prediction, action reasoning, and Physical AI.
Cosmos3-Super	64B	Frontier-scale omnimodal world model for advanced multimodal understanding, world simulation, future prediction, action reasoning, and Physical AI.
Cosmos3-Super-Text2Image	64B	High-fidelity text-to-image generation.
Cosmos3-Super-Image2Video	64B	Temporally coherent image-to-video generation.
Cosmos3-Nano-Policy-DROID	16B	Vision-language robot policy for DROID manipulation and control.

Supported Generation Settings

Setting	Supported values
Resolution tiers	256p, 480p, 720p, default=480p
Aspect ratios	16:9, 4:3, 1:1, 3:4, 9:16, default=16:9
Frame rates	10, 16, 24, and 30 FPS, default=24
Frame count	5 to 300 frames, default=189
Precision	BF16 tested
Operating system	Linux
GPU architectures	NVIDIA Ampere, Hopper, and Blackwell

Input and Output

Spec	Value
Input types	Text, text + image, text + video, text + image + action
Input formats	Text string, JPG/PNG/JPEG/WEBP image, MP4 video, JSON action array
Vision conditioning	720p uses 1280x720, 480p uses 832x480, and 256p uses 320x192. Video conditioning uses 5 frames at the matching resolution.
Action conditioning	Supported action dimensions depend on the embodiment, including camera motion (9D), autonomous vehicle (9D), egocentric motion (57D), single-arm robot (10D, DROID/UR/Fractal/Bridge/UMI), dual-arm robot (20D, dual DROID arms), humanoid robot (29D, AgiBot).
Output types	Image, video, sound, action state, text
Output formats	JPG image, MP4 video, AAC sound stream muxed into MP4, JSON action values, text string
Prompt length	Fewer than 300 words is recommended for world-generation prompts
Sound output	Stereo AAC at 48 kHz when generated with video

Use Cases

Generator

Generator examples produce non-text outputs conditioned by text, vision, and action inputs.

Workflow	Inputs	Outputs	What it demonstrates
Text-to-image	Text	Vision	Robotics laboratory scene generation from a text prompt
Text-to-video	Text	Vision	Industrial video generation from a dense scene description
Text-to-video with sound	Text	Vision, sound	Synchronized visual and audio generation
Image-to-video	Text, image	Vision	Robot manipulation animation from a starting image and prompt
Image-to-video with sound	Text, image	Vision, sound	Image-conditioned motion with synchronized audio
Video-to-video	Text, video	Vision	Prompt-guided transformation of a robot manipulation video
Video-to-video with sound	Text, video, sound	Vision, sound	Prompt-guided transformation of a robot manipulation video
Forward dynamics	Text, vision, action	Vision	Future-state rollout from action and visual context
Action policy	Text, vision	Action, vision	Action trajectories and rollout video from context

Generator prompt upsampling expands short scene descriptions into dense structured prompts. The current examples use these sampling defaults:

Parameter	Value
max_tokens	20000
temperature	0.7
top_p	0.8
top_k	20
repetition_penalty	1.0
presence_penalty	1.5
seed	3407

Reasoner

Reasoner examples produce text outputs from text and vision inputs. It follows Qwen3-VL-compatible message conventions for image and video inputs.

Workflow	Inputs	Outputs	What it demonstrates
Caption	Video	Text	Detailed video captioning
Temporal localization	Video, query	Text or JSON	Event detection, timestamp query, and interval question answering
Embodied reasoning	Video, question	Text	Next-action prediction for robotics and assisted-task settings
Common-sense reasoning	Video, question	Text	Physical common-sense judgment with visible context
2D grounding	Image, prompt	JSON boxes	Bounding-box localization from an image prompt
Describe anything	Image, marked subjects	JSON or text	Attribute captioning for marked subjects
Action CoT	Image or video, prompt	Text or JSON	Trajectory prediction and driving-scene chain-of-thought
Physical Plausibility Analysis	Video, prompt	Label	Physical plausibility classification
Situation Understanding	Video, question	Text	Situation understanding and likely-next-action prediction

Reasoner examples use the following sampling settings:

Parameter	Without reasoning	With reasoning
top_p	0.8	0.95
top_k	20	20
repetition_penalty	1.0	1.0
presence_penalty	1.5	0.0
temperature	0.7	0.6

Use this basic message shape for text + vision requests:

[
  {
    "role": "system",
    "content": [{"type": "text", "text": "You are a helpful assistant."}]
  },
  {
    "role": "user",
    "content": [
      {"type": "video_url", "video_url": "https://example.com/video.mp4"},
      {"type": "text", "text": "List the notable events with approximate timestamps."}
    ]
  }
]

For explicit reasoning, append this format instruction to the user prompt:

Answer the question using the following format:

<think>
Your reasoning.
</think>

Write your final answer immediately after the </think> tag.

Quickstart

Before running examples, create a Hugging Face access token and then authenticate locally:

uvx hf@latest auth login

Set HF_HOME if you want to use a shared cache or a disk with more space.

Generator with Diffusers

Expand Diffusers generator setup, example, and modes

Use HuggingFace Diffusers for Cosmos 3 Generator research, training, and model development. This path loads the full Cosmos 3 checkpoint, including the reasoner path, diffusion generation path, and media tokenizers.

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

--torch-backend=auto lets uv detect your NVIDIA driver and install a matching CUDA build of torch/torchvision. Without it, uv pulls the newest CUDA wheel (currently cu130), which fails on pre-CUDA-13 drivers with The NVIDIA driver on your system is too old and torch.cuda.is_available() returns False. Pin an explicit backend instead if you prefer, e.g. --torch-backend=cu128 for a CUDA 12.8 driver.

A text-to-video run takes a while: the first run downloads Cosmos3-Nano, and diffusion is compute-heavy, running through every inference step before producing output. Long step times are expected, not a hang.

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",
)

result = pipe(
    prompt="A mobile robot navigates a warehouse aisle and stops at a shelf.",
    num_frames=189,
    height=720,
    width=1280,
    fps=24.0,
)

export_to_video(result.video, "cosmos3_t2v.mp4", fps=24, macro_block_size=1)

Diffusers modes:

Mode	Use
text-to-image	Single-frame image generation with num_frames=1; returns a PIL image
text-to-video	Video generation; 189 frames is about 7.9 seconds at 24 FPS
image-to-video	Video generation conditioned on an input image
text-to-video-with-sound	Video generation with sound for checkpoints that include sound modules

See the Cosmos 3 Diffusers documentation for runnable examples of each mode.

Generator with vLLM-Omni

Expand vLLM-Omni generator setup, endpoints, and request reference

Use vLLM-Omni for Generator production inference behind an OpenAI-compatible API. This integration loads the full Cosmos 3 checkpoint, including the Qwen3-VL-based reasoner path and the diffusion generation path. For understanding-only tasks that return text, use Reasoner with vLLM instead, which loads only the reasoner.

Compatibility status: Cosmos 3 Generator support is being upstreamed in vllm-project/vllm-omni#3454, which adds text-to-image, text-to-video, and image-to-video; follow-up PRs add video-to-video, video-with-sound, and action. Until they merge, the vllm/vllm-omni:cosmos3 Docker image is the official build with every modality supported; the PR-branch install below covers only the three merged modes.

Start the server from the Docker image (all modalities). Mount any directory that contains local media or action files you want the server to read.

docker run --runtime nvidia --gpus all \
  -v ~/.cache/huggingface:/root/.cache/huggingface \
  -v "$(pwd):/workspace" \
  -p 8000:8000 \
  --ipc=host \
  vllm/vllm-omni:cosmos3 \
  vllm serve nvidia/Cosmos3-Nano \
  --omni \
  --model-class-name Cosmos3OmniDiffusersPipeline \
  --allowed-local-media-path / \
  --port 8000

vLLM-Omni prints Application startup complete. when the API is ready.

For nvidia/Cosmos3-Super (the larger 64B model), split weights across GPUs and optionally offload layers to reduce peak memory: --tensor-parallel-size splits model weights across multiple GPUs, and --enable-layerwise-offload offloads transformer blocks between CPU and GPU with a latency tradeoff and extra CPU RAM use. For example, on four GPUs, add --tensor-parallel-size 4 --enable-layerwise-offload to the vllm serve command.

Additional parallelism options:

Option	Use
--cfg-parallel-size 2	Runs the positive and negative CFG branches in parallel on two GPUs. Set CFG strength with the request-level guidance_scale; do not use true_cfg_scale.
--ulysses-degree 2	Enables Ulysses sequence parallelism, splitting the sequence dimension across GPUs.

When combining parallelism options, ensure the server has enough GPUs for the product of the enabled degrees (tensor_parallel_size × cfg_parallel_size × ulysses_degree).

To install the three merged modes (text-to-image, text-to-video, image-to-video) from the upstreaming PR branch instead of using the Docker image, create a venv and install vLLM-Omni from the PR ref, choosing the CUDA build that matches your driver:

uv venv --python 3.13 --seed --managed-python
source .venv/bin/activate
# CUDA 13 driver:
uv pip install --torch-backend=cu130 \
  "vllm-omni @ git+https://github.com/vllm-project/vllm-omni.git@refs/pull/3454/head"
# CUDA 12.8 driver:
# uv pip install --torch-backend=cu128 \
#   "vllm-omni @ git+https://github.com/vllm-project/vllm-omni.git@refs/pull/3454/head"

Then run vllm serve nvidia/Cosmos3-Nano --omni --model-class-name Cosmos3OmniDiffusersPipeline --allowed-local-media-path / --port 8000 directly, without the docker run ... vllm/vllm-omni:cosmos3 wrapper.

Vision endpoints:

Mode	Endpoint	Notes
Text to image	POST /v1/images/generations	Returns a base64-encoded PNG
Text to video	POST /v1/videos/sync	Blocks and returns the MP4 bytes directly
Image to video	POST /v1/videos/sync	Upload the conditioning image with input_reference
Video to video	POST /v1/videos/sync	Upload a source video and choose which frames stay as clean conditioning
Video with sound	POST /v1/videos/sync	Add generate_sound=true to produce a soundtrack alongside the video

Action modes use Cosmos 3 as a world model: they condition on an embodiment (domain_name) and exchange video and action sequences. Policy and inverse dynamics return a predicted action chunk, so send those through the asynchronous POST /v1/videos job and read the action data from the completed result; forward dynamics returns only video and can use synchronous POST /v1/videos/sync.

Mode	action_mode	Input	Output
Policy	policy	Image + instruction	Video + predicted action chunk
Inverse dynamics	inverse_dynamics	Video + instruction	Video + predicted action chunk
Forward dynamics	forward_dynamics	Image + action chunk	Video

Pass embodiment settings through extra_params: action_mode, domain_name (for example bridge_orig_lerobot, av, or camera_pose), raw_action_dim, and action_chunk_size. Forward dynamics also takes an action_path pointing at an action file the server can read, so start the server with --allowed-local-media-path covering that file (for Docker, mount the file and pass the container-visible path). For the full set of robot, autonomous-vehicle, and camera-pose variants, see the Cosmos 3 online-serving examples.

Example video request:

curl -sS -X POST http://localhost:8000/v1/videos/sync \
  --form-string "prompt=A small warehouse robot moves a blue box across a clean floor." \
  --form-string "negative_prompt=blurry, distorted, low quality" \
  --form-string "size=1280x720" \
  --form-string "num_frames=81" \
  --form-string "fps=24" \
  --form-string "num_inference_steps=35" \
  --form-string "guidance_scale=4.0" \
  --form-string "seed=42" \
  -o cosmos3_t2v_output.mp4

Use --form-string for text fields (prompt, negative_prompt, extra_params) rather than -F: with -F, curl treats ; as a content-type separator and silently truncates any value that contains one.

Common request fields (the image endpoint follows the Image Generation API, and the video endpoints follow the Videos API):

Field	Purpose
prompt	Positive text prompt
negative_prompt	Concepts or artifacts to avoid
size	Output resolution as <width>x<height>
num_frames, fps	Video length and frame rate (video endpoints only)
num_inference_steps	Diffusion denoising steps
guidance_scale	Classifier-free guidance scale (use this for Cosmos 3 CFG; do not use true_cfg_scale)
flow_shift	Scheduler flow-shift value
seed	Reproducibility seed
max_sequence_length	Maximum number of prompt tokens kept for conditioning (Cosmos 3 default 512); longer prompts are truncated with a warning, shorter ones padded
input_reference	Uploaded image or video for image-to-video, video-to-video, and action requests
extra_params	JSON-encoded Cosmos 3-specific options: action settings (action_mode, domain_name, raw_action_dim, action_chunk_size, action_path), video-to-video conditioning (condition_frame_indexes_vision, condition_video_keep), prompt-template toggles (use_resolution_template, use_duration_template), and the per-request guardrails toggle
extra_args	JSON object for Cosmos 3-specific image-endpoint options such as use_resolution_template

Disabling guardrails: Cosmos 3 ships safety guardrails that screen prompts and blur faces in generated output. Disable them per request by adding guardrails: false to extra_params:

curl -sS -X POST http://localhost:8000/v1/videos/sync \
  --form-string "prompt=A small warehouse robot moves a blue box across a clean floor." \
  --form-string 'extra_params={"guardrails":false,"use_resolution_template":false,"use_duration_template":false}' \
  -o cosmos3_t2v.mp4

To disable guardrails server-wide so the guardrail models are never loaded (per-request overrides then cannot turn them back on), pass a deploy config — a future release replaces this with a dedicated --cosmos3-no-guardrails flag:

# no_guardrails.yaml
async_chunk: false
stages:
  - stage_id: 0
    max_num_seqs: 1
    enforce_eager: true
    trust_remote_code: true
    model_class_name: Cosmos3OmniDiffusersPipeline
    model_config:
      guardrails: false
      offload_guardrail_models: false

vllm serve nvidia/Cosmos3-Nano --omni \
  --model-class-name Cosmos3OmniDiffusersPipeline \
  --deploy-config no_guardrails.yaml \
  --port 8000

References:

• Cosmos 3 online-serving examples
• vLLM-Omni Videos API
• vLLM-Omni Image Generation API
• Cosmos 3 vLLM-Omni upstreaming PR

Reasoner with Transformers

Coming soon!

Reasoner with vLLM

Use vLLM for Reasoner production inference behind an OpenAI-compatible chat-completions API.

uv venv --python 3.13 --seed --managed-python
source .venv/bin/activate
uv pip install --torch-backend=cu130 "vllm==0.21.0" \
  "vllm-cosmos3 @ git+https://github.com/NVIDIA/cosmos-framework.git#subdirectory=packages/vllm-cosmos3"

The vLLM version and the torch backend are paired: use --torch-backend=cu130 "vllm==0.21.0" for a CUDA 13 driver, or --torch-backend=cu128 "vllm==0.19.1" for CUDA 12.8. vLLM does not publish wheels for every CUDA minor version, so --torch-backend=auto is not reliable here — pick the pair that matches your driver.

vllm serve nvidia/Cosmos3-Nano \
  --hf-overrides '{"architectures": ["Cosmos3ReasonerForConditionalGeneration"]}' \
  --async-scheduling \
  --allowed-local-media-path / \
  --port 8000

If your vLLM build reports that DeepGEMM is unavailable, disable it before starting the server:

export VLLM_USE_DEEP_GEMM=0

Configuration notes:

Option	Use
--tensor-parallel-size	Number of GPUs used for tensor parallel inference
--mm-encoder-tp-mode data	Data parallelism for the visual encoder in multimodal workloads
--media-io-kwargs '{"video": {"num_frames": -1}}'	Allows the processor to consider all available frames before downstream frame sampling
--allowed-local-media-path	Required when requests pass local file:// media paths

Troubleshooting

Which CUDA version should I use?

CUDA 13 (recommended) or 12.8. Your system CUDA and PyTorch's CUDA major version must match — check with nvidia-smi and python -c "import torch; print(torch.version.cuda)".

Which base container should I use?

NVIDIA NGC PyTorch: nvcr.io/nvidia/pytorch:25.09-py3 for CUDA 13, or nvcr.io/nvidia/pytorch:25.06-py3 for CUDA 12.

torch.cuda.is_available() is False ("The NVIDIA driver on your system is too old")

The installed torch is newer CUDA than your driver — uv pip install torch defaults to CUDA 13 (cu130). Install a matching build: uv pip install --torch-backend=auto torch torchvision (or pin, e.g. --torch-backend=cu128). For uv sync notebooks use COSMOS3_UV_GROUP=cu128-train; for vLLM pair cu128 with vllm==0.19.1.

Import fails with libxcb.so.1: cannot open shared object file

On headless servers and minimal containers, importing or running a pipeline can fail with libxcb.so.1: cannot open shared object file (or another missing graphics library), because a dependency links against system X11/graphics libraries that are not installed. Install them:

apt-get install -y libxcb1 libgl1 libglib2.0-0

uv errors on install or sync

The Cosmos Framework requires uv >= 0.11.3 (enforced via its pyproject.toml). Older versions fail to parse the project config (for example the [tool.uv.audit] section) and do not recognize newer --torch-backend values such as cu130 (you may see a value is required for '--torch-backend' or an accepted-values list that stops at cu129). Upgrade with uv self update (or reinstall from https://astral.sh/uv).

Choosing an Integration

Goal	Use	Notes
Generator research or model development	Diffusers	Python-first path for inspecting and modifying generator behavior
Generator production inference	vLLM-Omni	API path for image, video, sound, and action outputs
Reasoner research or model development	Transformers (coming soon)	Python-first path for prompts, processors, and model behavior
Reasoner production inference	vLLM	OpenAI-compatible endpoint for text outputs from text and vision inputs
Runnable setup, training, or evaluation	Cosmos Framework	Full workflow docs for setup, inference, omni-model training, and evaluation

Examples

We are building examples that show Cosmos 3 capabilities end to end, including world generation, world understanding, captioning, temporal localization, grounding, and physical reasoning. Each example is a self-contained script or notebook you can run from this repository.

Example	Surface	Workflows demonstrated	Open	nbviewer
Generator (audiovisual) with Diffusers	Generator	Text-to-image, plus text-to-video and image-to-video each with or without synchronized sound, via Cosmos3OmniPipeline.	Notebook
Generator (audiovisual) with Cosmos Framework	Generator	Text-to-image, plus text-to-video and image-to-video each with sound on or off, through the cosmos_framework.scripts.inference entrypoint.	Notebook
Generator (audiovisual) with vLLM-Omni	Generator	Text-to-image, plus text-to-video and image-to-video each with sound on or off, against an OpenAI-compatible vLLM-Omni server.	Notebook
Forward dynamics with Cosmos Framework	Generator	Forward dynamics: action-conditioned future-observation prediction for AV, DROID, and UMI, through the cosmos_framework.scripts.inference entrypoint.	Notebook
Forward dynamics with vLLM-Omni	Generator	Forward dynamics: action-conditioned future-observation prediction for AV, DROID, and UMI, against an OpenAI-compatible vLLM-Omni server.	Notebook
Inverse dynamics with Cosmos Framework	Generator	Inverse dynamics: ego-motion trajectory prediction from input AV video, through the cosmos_framework.scripts.inference entrypoint.	Notebook
Inverse dynamics with vLLM-Omni	Generator	Inverse dynamics: ego-motion trajectory prediction from input AV video, against an OpenAI-compatible vLLM-Omni server.	Notebook
Reasoner with Cosmos Framework	Reasoner	Text and image reasoning: detailed captioning, robot task planning, 2D grounding, describe-anything, and action-trajectory prompts, through the cosmos_framework.scripts.inference entrypoint.	Notebook
Reasoner with vLLM	Reasoner	Image and video reasoning: captioning, temporal localization, embodied reasoning, common-sense reasoning, 2D grounding, describe-anything, action CoT, driving scenes, physical-plausibility, and situation understanding, against an OpenAI-compatible vLLM server (Cosmos3-Super on 4 GPUs by default; switch to Nano per the cookbook README).	Notebook

Inference Benchmarks

Cosmos 3 latency and serving numbers live in inference_benchmarks.md. Generator sections report diffusion-path latency (seconds) by GPU, engine, resolution, and tensor-parallel width; the Reasoner section reports vLLM serving metrics under concurrent load. Empty cells mean a combination has not been measured yet, not that it is unsupported.

Benchmark	Surface	Model	What it covers
Cosmos3-Nano generator	Generator	Cosmos3-Nano	Text-to-image, text-to-video, and image-to-video latency across PyTorch, vLLM-Omni, and Diffusers
Cosmos3-Super generator	Generator	Cosmos3-Super	The same modalities and engines at the larger checkpoint scale
Cosmos3-Nano reasoner	Reasoner	Cosmos3-Nano	vLLM serving metrics — TTFT, request latency, and throughput at concurrency 1/64/128/256

Limitations

Cosmos 3 can produce artifacts in long, high-resolution, or physically complex outputs. Common failure modes include temporal inconsistency, unstable camera or object motion, inaccurate sound-video alignment, imperfect action-state consistency, object morphing, inaccurate 3D structure, and implausible physical dynamics. Applications that require physically grounded simulation, safety-critical control, or complex multi-agent behavior need additional validation, guardrails, and system-level safety analysis before deployment.

Ecosystem

Project	Purpose
Cosmos Framework	End-to-end Physical AI framework for training and serving world models, including setup, inference, and training
Cosmos Curator	Distributed Physical AI data curation system covering processing, annotation, filtering, and deduplication
Cosmos Evaluator	Automated Physical AI evaluation system for world generation and world reasoning outputs

News

• [May 31, 2026] We released Cosmos 3 in the NVIDIA Cosmos 3 Hugging Face collection and Cosmos Framework which provides runnable setup, inference, training, and evaluation workflows for Cosmos models. See the Cosmos 3 Technical Report.

License and Contact

This project may download and install additional third-party open source software projects. Review the license terms of those projects before use.

NVIDIA Cosmos source code and models are released under, and subject to the terms of, the OpenMDW-1.1 License. For a custom license, contact cosmos-license@nvidia.com.