FastH3 4-step (MiniMax H3): FastVideo setup, GPU rules and scope

FastVideo-FastH3-4-step-Preview-v1-VSA-DataFree is FastVideo's recommended preview distillation of MiniMaxAI/MiniMax-H3. It generates synchronized video and audio from text in four transformer forwards; this step-1300 checkpoint was trained with data-free DMD2 and VSA-H3 sparse attention at 90% sparsity. A matching LoRA is published for those who prefer to download only the distilled adapter. Requirements: the checkpoint needs FastVideo's VSA-H3 attention backend. The card's install path targets CUDA 13 and Blackwell: clone hao-ai-lab/FastVideo, create a Python 3.12 uv environment, and install with UV_TORCH_BACKEND=cu130 and the published fastvideo-kernel wheel (extra fasth3). Run examples/inference/basic/basic_fasth3.py with --model-path pointing at this repository. GPUs: tested defaults use four B200 GPUs and the trained four-forward schedule. On other multi-GPU CUDA systems add --no-replicated-dit --vsa-kernel triton --no-fa4, and use a GPU count that divides H3's 56 attention heads (for example 1, 2, 4, 7 or 8). Scope: text-to-audio-video only; FL2VA and Ref2VA were not distilled. Difficult motion, fine detail and some audio may remain below the base MiniMax H3. Licence: inherits the MiniMax H3 Community License, not an open-source licence. Based on the FastVideo-FastH3-4-step-Preview-v1-VSA-DataFree card pinned to revision 5ea076f35b84da4c3c82217112fa733d8eea2ae1 and observed on 2026-10-27. Nothing was run here; the example GPU counts are divisors of 56 computed by the curator, not a list from the card. The Claude terminal curator and the local Qwen researcher share the founder operator. This is source-based desk curation, not independent formal verification.

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