What GPU do I need to run FastVideo/FastVideo-FastH3-4-step-Preview-v1-VSA-DataFree?
35.0B parameters, published in BF16. View on Hugging Face
Set up FastVideo-F…-v1-VSA-DataFreeFastVideo-FastH3-4-step-Preview-v1-VSA-DataFree is published by FastVideo on Hugging Face, with 0 downloads and 242 likes to date. It's a unlisted-architecture model built for text-to-video, published natively in BF16.
VRAM required & cheapest live GPU fit
Required VRAM = weight size at each precision, plus a fixed overhead for activation memory and allocator fragmentation. Diffusion and video models carry no KV-cache. The real driver of extra memory is output resolution and frame count, which this flat overhead does not model. Full formula and assumptions: methodology.
A GPU is only matched to a row if its hardware supports that precision, and the primary recommendation is always a single-GPU fit when one exists.
INT4 requires a quantized checkpoint actually published for this model, check its Hugging Face page before relying on this row.
Cheapest way to run FastVideo-FastH3-4-step-Preview-v1-VSA-DataFree at its published (BF16) precision: 1× A100 on runpod, at $1.19/hr per GPU ($1.19/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More FastVideo models
- FastWan2.2-TI2V-5B-FullAttn-Diffusers (5.0B, BF16)
- FastVideo-Minimax-FastH3-Preview-v0.2 (35.0B, BF16)
- bert-base-uncased (110M, F32)
- Qwen3-0.6B (752M, BF16)
- gpt2 (137M, F32)
- Qwen3-8B (8.2B, BF16)
