Add NIM inference path for the Cosmos 3 Generator#258
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nv-asotelo
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Jul 2, 2026
| | [vLLM](#vllm) | OpenAI-compatible reasoning server (image/video understanding) | Reasoner | | ||
| | [vLLM-Omni](#vllm-omni) | OpenAI-compatible generation server (image/video/audio/action) | Generator (Audiovisual, Action) | | ||
| | [NIM](#nim) | Prebuilt OpenAI-compatible reasoning server (image/video understanding); no venv | Reasoner | | ||
| | [NIM](#nim) | Prebuilt NGC containers; Reasoner serving plus Generator audiovisual T2V/I2V only | Reasoner, Generator (Audiovisual) | |
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Use the same naming conventions as above (image/video/audio). Since this reflects two separate NIMs for now, use (video to text reasoning) for reasoner.
nv-asotelo
reviewed
Jul 2, 2026
| - [`uv`](https://docs.astral.sh/uv/getting-started/installation/), `git`, and `git-lfs` installed. | ||
| - Hugging Face access to the gated Cosmos3 model repos. Generator also requires | ||
| access to the gated | ||
| - Hugging Face access to the gated Cosmos3 model repos for Hugging Face based |
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be more explicit, or state all supported backends except NIM.
Make sure to remember SGLang and TRT-LLM if making access claims.
nv-asotelo
reviewed
Jul 2, 2026
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| A prebuilt container that serves **Cosmos3-Generator Text2Video and Image2Video | ||
| only** through `POST /v1/infer`. The NIM infers mode from the request fields: | ||
| non-empty `prompt` with no `image` means T2V; `image` provided means I2V. The |
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Explicit - Text-to-video, image-to-video
nv-asotelo
reviewed
Jul 2, 2026
| - **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. | ||
| - **Research and production paths:** Use Diffusers and Transformers for Python-first development, vLLM-Omni and vLLM for OpenAI-compatible serving, and NIM containers for turnkey Reasoner serving plus Generator T2V/I2V deployment. |
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explicitly, "text to video" and "image to video". This is a different readme file and the first time the acronym is presented.
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@ekrivovnv I added some comments, just minor nits. Please address and LGTM |
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@nv-asotelo I think all your comments were addressed |
qmiao-hub
requested review from
rickzw and
yaoxu-crypto
and removed request for
nv-asotelo
July 14, 2026 22:07
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Added tutorial on using NIM for the Generator path.
Also, added benchmarks for the Generator, overall they show perfomance improvement over vllm-omni.