NVIDIA Cosmos | 🤗 Cosmos 3
Part of the NVIDIA Cosmos project family — the training and serving framework repository.
# Cosmos-Framework **Cosmos-Framework** is an end-to-end framework for training and serving world models, including the **Cosmos3** model family. Everything lives in a single top-level [`cosmos_framework/`](./cosmos_framework) Python package: - **Training** — distributed FSDP / TP / CP / PP trainer, native DCP checkpoints with HuggingFace `safetensors` import/export, JSONL / WebDataset / LeRobot dataset adapters. Entry point: `cosmos_framework.scripts.train`. See [`docs/training.md`](./docs/training.md). - **Inference** — Diffusers / Transformers / vLLM backends with offline batch generation and online serving (Ray + Gradio). Entry point: `cosmos_framework.scripts.inference`. Ecosystem-facing shim libraries (lightweight standalone wrappers for downstream projects) live under [`packages/`](./packages). ## Cosmos 3 **Cosmos 3** is our newest model family [[Report]](https://research.nvidia.com/labs/cosmos-lab/cosmos3/technical-report.pdf) [[Website]](https://research.nvidia.com/labs/cosmos-lab/cosmos3/). 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. For a guided experience to test out Cosmos3, please visit [[Cosmos]](https://github.com/nvidia/cosmos). ## Framework Documentation - [Quickstart](#setup) - [Setup](./docs/setup.md) - [Training (Supervised Fine-Tuning)](./docs/training.md) - [JSONL Dataset](./docs/dataset_jsonl.md) - [Inference](./docs/inference.md) - [Policy Server](./docs/action_policy_droid_server.md) - Reference - [Code Structure](./docs/code_structure.md) - [Environment Variables](./docs/environment_variables.md) - [FAQ](./docs/faq.md) - [AGENTS.md](./AGENTS.md) ## Setup For more details and alternative installation methods, see [Setup](./docs/setup.md#installation). Before installing, make sure your machine meets the [System Requirements](./docs/setup.md#system-requirements). If you want a curated PyTorch + CUDA environment, start from the [recommended NVIDIA NGC base image](./docs/setup.md#recommended-base-image). Install system dependencies: ```shell sudo apt-get install -y --no-install-recommends curl ffmpeg git-lfs libx11-dev tree wget ``` Install the package with `uv` (pick the dependency group that matches your CUDA toolkit — see [CUDA Variants](./docs/setup.md#cuda-variants)): ```shell # CUDA 13.0 (recommended) uv sync --all-extras --group=cu130-train # Or, for CUDA 12.8: # uv sync --all-extras --group=cu128-train source .venv/bin/activate && export LD_LIBRARY_PATH= ``` If you are starting from the recommended NGC image (`nvcr.io/nvidia/pytorch:25.09-py3`), see the [one-shot quickstart](./docs/setup.md#quickstart-from-the-recommended-base-image). ## Training For the full guide (data preparation, base-checkpoint conversion, parallelism strategies, mixed precision, resuming), see [Training](./docs/training.md). The number of GPUs required depends on the recipe; the shipped recipes under [`examples/`](./examples/README.md) are 8-GPU configurations (tested on 8× H100 80 GB) launched via their paired launch shells, e.g.: ```shell bash examples/launch_sft_vision_nano.sh ``` Users may adjust the GPU count to match their model and underlying hardware architecture — tune `NPROC_PER_NODE` and the parallelism degrees (DP/CP/FSDP shard) in the recipe accordingly. ## Inference See [Inference](./docs/inference.md) for the full guide — launch commands, supported modes, parallelism presets, and troubleshooting. Quick single-GPU launch: ```shell python -m cosmos_framework.scripts.inference \ --parallelism-preset=latency \ -i "inputs/omni/t2v.json" \ -o outputs/omni_nano \ --checkpoint-path Cosmos3-Nano \ --seed=0 ``` ## Policy Server See [Policy Server](./docs/action_policy_droid_server.md) for the full guide. ## Reference | Topic | What it covers | | ------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------ | | [Setup](./docs/setup.md) | Hardware/software prerequisites, `uv` install paths, CUDA variants, Docker base image, and base-checkpoint downloading. | | [Code Structure](./docs/code_structure.md) | Repository layout and a per-subpackage tour of `cosmos_framework/` — where each concern lives and where to add new code. | | [Training](./docs/training.md) | Launching multi-GPU and multi-node runs; parallelism strategies; mixed precision; resuming. | | [Inference (from a trained checkpoint)](./docs/inference.md) | Loading a trained checkpoint into one of the inference backends. | | [Policy Server](./docs/action_policy_droid_server.md) | Running the server-client pipeline for Cosmos3-Nano-Policy-DROID. | | [FAQ](./docs/faq.md) | Troubleshooting (OOM, NCCL hangs, slow training), environment variables, and common pitfalls. |