What GPU do I need to run ali-vilab/i2vgen-xl?
1.4B parameters, published in F32. View on Hugging Face
Set up i2vgen-xli2vgen-xl is published by ali-vilab on Hugging Face, with 35,228 downloads and 186 likes to date. It's a unlisted-architecture model built for text-to-video, published natively in F32.
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.
| Precision | Weight size | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| FP32 | 5.3 GB | 6.3 GB | RTX 3070 | 1 | $0.088/hr |
| FP8 (quantized) | 1.3 GB | 1.6 GB | RTX 4070 | 1 | $0.121/hr |
| INT4 (quantized) | 0.7 GB | 0.8 GB | RTX 3070 | 1 | $0.088/hr |
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 i2vgen-xl at its published (F32) precision: 1× RTX 3070, at $0.088/hr per GPU ($0.088/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
i2vgen-xl: common questions
Does i2vgen-xl fit on a 8 GB GPU?
Yes. At FP32 it needs 6.3 GB of VRAM, so an 8 GB card holds it with 1.7 GB to spare. A 6 GB card is not enough for it at FP32.
Can i2vgen-xl run in 16-bit instead of FP32?
Yes. Its published weights are FP32, 5.3 GB, or 6.3 GB once inference overhead is added. Loading the same weights in 16 bits halves that to 2.6 GB, or 3.2 GB with overhead. That moves it onto a 6 GB card instead of an 8 GB one. How much accuracy the cast costs is model-specific and is not measured here.
What is the least VRAM i2vgen-xl can run in?
0.8 GB, at INT4 (quantized), which fits a 6 GB card, against 6.3 GB at FP32. That row assumes an INT4 checkpoint has actually been published for this model, so check its Hugging Face page before planning around it.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More ali-vilab models
- text-to-video-ms-1.7b (1.4B, F32)
- bert-base-uncased (110M, F32)
- Qwen3-0.6B (752M, BF16)
- gpt2 (137M, F32)
- Qwen3-8B (8.2B, BF16)
- Qwen3.6-35B-A3B-FP8 (36.0B, F8_E4M3)
Related reading: RTX 3070 pricing and specs, The NVIDIA Inception program, explained, Google Colab alternatives for dedicated GPU access, Free GPU credits for students and researchers, and RunPod volume disk vs. network volume, compared.
