FastH3-QAD

FastH3-QAD-v2 is an 8-step video-and-audio model with NVFP4 weights, trained with quantization-aware distillation (QAD). It runs on one Blackwell GPU (RTX 5090, RTX PRO 6000) or a DGX Spark.

• Transformer: 50 blocks, NVFP4 W4A4 on all attention q/k/v/out and MLP linears, plus the sparse-attention (VSA) gates. The q/k/v/out and MLP weights were trained with QAD. The VSA gates and all norms stay in BF16.
• Attention: block-sparse VSA with 64-token tiles and sparsity 0.8. The checkpoint was trained with BF16 VSA. FastVideo also exposes an FP4 VSA inference lane on Blackwell.
• 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.

Quantization-aware distillation

QAD trains the NVFP4 student against a frozen BF16 teacher along the teacher's 8-step trajectory. The student runs the deployed 4-bit weights and activations, so it learns weights that work with the quantized forward pass. Training uses text prompts only: 300 steps, learning rate 5e-6, with 25% of rows on the student's own trajectory. Only the quantized linears train. The VSA gates and norms are frozen because changing them changes which video tiles the model attends to, not just the arithmetic of attention.

Error against the BF16 teacher on 30 held-out prompts (relative L2 of the x0 prediction, lower is better):

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/FastH3-QAD-v2", "transformer/adaln_tables.pt")

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