What GPU do I need to run kandinskylab/Kandinsky-6.0-Pro-distill-5s-Diffusers?
30.1B parameters, published in BF16. View on Hugging Face
Set up Kandinsky-6…ill-5s-DiffusersKandinsky-6.0-Pro-distill-5s-Diffusers is published by kandinskylab on Hugging Face, with 187 downloads and 18 likes to date. It's a unlisted-architecture model built for image-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.
| Precision | Weight size | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| BF16 | 56.1 GB | 67.4 GB | A100 | 1 | $1.21/hr |
| cheaper alt. | RTX A5000 | 3 | $0.528/hr | ||
| FP8 (quantized) | 28.1 GB | 33.7 GB | RTX 4090 | 1 | $0.481/hr |
| cheaper alt. | RTX 4070 | 3 | $0.363/hr | ||
| INT4 (quantized) | 14.0 GB | 16.8 GB | RTX A5000 | 1 | $0.176/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 Kandinsky-6.0-Pro-distill-5s-Diffusers at its published (BF16) precision: 1× A100, at $1.21/hr per GPU ($1.21/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
Kandinsky-6.0-Pro-distill-5s-Diffusers: common questions
Can Kandinsky-6.0-Pro-distill-5s-Diffusers run on a single GPU?
Yes, but not on a desktop card. At BF16 it needs 67.4 GB of VRAM, more than a single 32 GB desktop card holds. The cheapest live single-card fit is a 80.0 GB A100 at $1.21/hr.
What is the least VRAM Kandinsky-6.0-Pro-distill-5s-Diffusers can run in?
16.8 GB, at INT4 (quantized), which fits a 24 GB card, against 67.4 GB at BF16. That row assumes an INT4 checkpoint has actually been published for this model, so check its Hugging Face page before planning around it.
Does quantizing Kandinsky-6.0-Pro-distill-5s-Diffusers lower the GPU bill?
Yes. At BF16 the cheapest live fit is one A100 at $1.21/hr. At INT4 (quantized) it drops to one RTX A5000 at $0.176/hr, provided a quantized checkpoint exists for it.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More kandinskylab models
- Kandinsky-6.0-Pro-5s-Diffusers (30.1B, BF16)
- 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: A100 pricing and specs, and The best GPUs for AI, ranked.
