---
license: other
base_model: FastVideo/FastVideo-FastH3-8-Step-V2
tags: [video-generation, text-to-video, audio, fastvideo, nvfp4]
---
# FastH3 V2, 8-step, NVFP4 for one GPU

FastH3 V2 (8 distilled steps, video with synchronized audio) packaged for a single Blackwell GPU (RTX 5090,
RTX PRO 6000) or a DGX Spark.

- **Transformer:** FastH3 V2 (50 blocks). MLP linears keep the calibrated NVFP4 weights and activation scales from
  [FastVideo/FastVideo-FastH3-8-Step-V2-NVFP4](https://huggingface.co/FastVideo/FastVideo-FastH3-8-Step-V2-NVFP4);
  attention projections and sparse-attention gates are also stored in NVFP4.
- **Text encoder:** NVFP4 Qwen3-VL trimmed to the 50 layers H3 reads.
- **VAE:** LynnReal lightweight video VAE with Kijai's INT8 weights; H3 audio VAE.
- **Sampling:** `fastvideo_inference.json` holds the 8-step DMD schedule, which FastVideo reads automatically.

## Machines with less than 64 GB of system RAM

The 50 AdaLN timestep projections are 26 GB in BF16. `transformer/adaln_tables.pt` (155 MB) holds their precomputed
outputs for the 8-step schedule, so FastVideo can skip loading them:

```python
import os
from huggingface_hub import hf_hub_download

os.environ["FASTVIDEO_H3_ADALN_TABLE"] = hf_hub_download(
    "FastVideo/FastVideo-FastH3-8-Step-V2-NVFP4-Consumer", "transformer/adaln_tables.pt")
```

The table covers only the shipped schedule (`fastvideo_inference.json`); FastVideo raises an error for other timesteps.

For multi-GPU data-center serving, use [FastVideo/FastVideo-FastH3-8-Step-V2-NVFP4](https://huggingface.co/FastVideo/FastVideo-FastH3-8-Step-V2-NVFP4).
Smaller, faster experimental variant: [FastVideo/FastVideo-FastH3-Trim-8-Step-NVFP4](https://huggingface.co/FastVideo/FastVideo-FastH3-Trim-8-Step-NVFP4).