Model reference · open weights

FastVideo-FastH3-8-Step-Consumer

Video FastVideo Text→video 1 build Its own licence terms 0 dl/mo

FastVideo-FastH3-8-Step-Consumer is an open-weight video model from FastVideo. FastVideo-FastH3-8-Step-V2-NVFP4-Consumer (NVFP4) weighs 63.1 GB; the smallest configuration that runs it is L40S 48 GB.

For self-hosting, compare this build's requirements below with the GPU you plan to rent.

  • FastVideo-FastH3-8-Step-Consumer is a 13.9B parameter text-to-video model developed by FastVideo.
  • It generates video with synchronized audio using an 8-step sampling schedule and is optimized to run on a single Blackwell GPU or DGX Spark.
  • The model uses NVFP4 quantization for its transformer and text encoder components, and it is released under an other licence.

Summary of the FastVideo/FastVideo-FastH3-8-Step-V2-NVFP4-Consumer model card, 2026-10-06

What it is

Released byFastVideo
Released2026-10-05
Parameters13.9B
VRAM63.1 GB for the weights

What it runs on

Memory and cards for FastVideo-FastH3-8-Step-V2-NVFP4-Consumer (NVFP4)

63.1 GBweights, file size
39.6 GBbiggest part
537 MBruntime overhead
CardWeightsMemory
RTX 3060 12 GB … RTX 5090 32 GBdoes not fit
L40S 48 GBtight (encoders offloaded)44.0 GB
A100 80 GBfits78.2 GB
H100 80 GBfits78.1 GB
RTX PRO 6000 Blackwell 96 GBfits93.8 GB
DGX Spark (GB10) 128 GB unifiedfits107 GB
H200 141 GBfits138 GB
B200 180 GBfits176 GB

From the model card

What FastVideo says about FastVideo-FastH3-8-Step-Consumer

Read the model card

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; 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:

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. Smaller, faster experimental variant: FastVideo/FastVideo-FastH3-Trim-8-Step-NVFP4.

Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.

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