---
license: other
license_name: openmdw-1.1
license_link: https://openmdw.ai/license/1-1/
base_model: nvidia/Cosmos3-Nano
base_model_relation: quantized
library_name: diffusers
pipeline_tag: text-to-image
tags: [cosmos, cosmos3, quantization, nvfp4, fp4, 4-bit, modelopt, blackwell, diffusers, text-to-image, image-to-video]
---

# Cosmos3-Nano — NVFP4-AWQ (4-bit, Blackwell-native)

A **NVFP4 (4-bit) weight-only, activation-aware** quantization of
[`nvidia/Cosmos3-Nano`](https://huggingface.co/nvidia/Cosmos3-Nano), produced with NVIDIA TensorRT
Model Optimizer. NVFP4 is the Blackwell-native 4-bit format (E2M1 with FP8 block scales). The
transformer's attention + FFN linears (**~11.8 B, 77.6%**) are NVFP4; embeddings, norms, the diffusion
time-embedder, and modality adapters stay **BF16**. Activations stay BF16 (weight-only).

> Derivative of `nvidia/Cosmos3-Nano`. © NVIDIA. Distributed under **OpenMDW-1.1** (license + NVIDIA
> copyright/origin notices retained, per the license). Not affiliated with, nor endorsed by, NVIDIA.

## Precision options (pick by hardware)
| Build | ~Total size | Fits 16 GB GPU? | Quality |
|---|---|---|---|
| **NVFP4-AWQ / INT4-AWQ (this tier)** | ~13 GB | ✅ (tight) | near-zero loss; hardest hands/text can wobble |
| [FP8](https://huggingface.co/Reza2kn/Cosmos3-Nano-FP8) | ~18 GB | ❌ (~24 GB) | near-indistinguishable from BF16 |
| BF16 ([original](https://huggingface.co/nvidia/Cosmos3-Nano)) | ~33 GB | ❌ | reference |

## Quality vs BF16 (96-prompt anatomy-weighted sweep)
| Metric | BF16 | NVFP4-AWQ |
|---|---|---|
| PickScore (human pref) | 21.85 | **21.82 (Δ −0.03)** |
| **FID vs BF16** | — | **80.6** (best distribution match of all 4-bit recipes) |
| Functional fidelity (velocity cosine) | 1.000 | ~0.998 |

**FID context:** BF16-vs-BF16 at a *different seed* (same prompts, N=96) = **138.6**. NVFP4-AWQ's 80.6 is
**well below that seed-noise floor** — it tracks BF16 more closely than BF16 tracks itself across seeds.
**Caveat:** dense interlocking hands / on-image text can still wobble (base-model-hard, present in BF16
too). See `worst_case_contact_sheet.png`.

## Usage
```python
import torch
from huggingface_hub import snapshot_download
from diffusers import Cosmos3OmniPipeline, Cosmos3OmniTransformer
import modelopt.torch.opt as mto

repo = snapshot_download("Reza2kn/Cosmos3-Nano-NVFP4-AWQ")
tf = Cosmos3OmniTransformer.from_config(
    Cosmos3OmniTransformer.load_config(f"{repo}/transformer/config.json")).to(torch.bfloat16)
mto.restore(tf, f"{repo}/transformer/modelopt_quantized.pt")
pipe = Cosmos3OmniPipeline.from_pretrained(
    repo, transformer=tf, torch_dtype=torch.bfloat16, enable_safety_checker=False).to("cuda")
with torch.autocast("cuda", dtype=torch.bfloat16):       # required (float32 rotary -> bf16 linears)
    img = pipe("A red panda astronaut floating in a nebula", num_frames=1, height=480, width=480).video[0][0]
```
Or `from load_quantized import load; pipe = load()`. **Requires** `diffusers` (git main/≥0.39),
`nvidia-modelopt`, `torch` cu128. Best on Blackwell (sm_120) for native NVFP4; runs elsewhere via modelopt dequant.

## Method
modelopt `NVFP4_AWQ_LITE_CFG` (awq_lite), weight-only; calibrated on multimodal image+video prompts through
the real denoising loop. Quantized `self_attn.*`/`mlp.*`/`mlp_moe_gen.*`/`lm_head`; BF16 for embeddings,
norms, `time_embedder`, `proj_in/out`, audio/action adapters.