What GPU do I need to run sd2-community/stable-diffusion-2-1?
866M parameters, published in F32. View on Hugging Face
Set up stable-diffusion-2-1stable-diffusion-2-1 is published by sd2-community on Hugging Face, with 11,061 downloads and 35 likes to date. It's a unlisted-architecture model built for text-to-image, 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 | 3.2 GB | 3.9 GB | V100 | 1 | $0.088/hr |
| FP8 (quantized) | 0.8 GB | 1.0 GB | RTX 4070 Super | 1 | $0.121/hr |
| INT4 (quantized) | 0.4 GB | 0.5 GB | RTX 4070 Super | 1 | $0.121/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 stable-diffusion-2-1 at its published (F32) precision: 1× V100, 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.
stable-diffusion-2-1: common questions
How much VRAM does stable-diffusion-2-1 need?
3.9 GB at FP32, 1.0 GB at FP8 (quantized), 0.5 GB at INT4 (quantized). All of those sit under 6 GB, the smallest capacity this page reasons about, so VRAM is not what limits where this model runs. The 3.2 GB of weights plus inference overhead is the whole requirement.
Can stable-diffusion-2-1 run in 16-bit instead of FP32?
Yes. Its published weights are FP32, 3.2 GB, or 3.9 GB once inference overhead is added. Loading the same weights in 16 bits halves that to 1.6 GB, or 1.9 GB with overhead. How much accuracy the cast costs is model-specific and is not measured here.
How many copies of stable-diffusion-2-1 fit on one V100?
4, by VRAM alone. That card carries 16.0 GB and one copy needs 3.9 GB at FP32, on a live rate of $0.088/hr for the whole card. Throughput is not modelled here, so 4 copies is not 4 times the requests served.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More Stable Diffusion 2 models
- sd-turbo (866M, F32)
- stable-diffusion-2-1-base (866M, F32)
- stable-diffusion-2-1-base (866M, F32)
- stable-diffusion-2 (866M, F32)
Alternatives at this size
Other models for text-to-image within about a third of stable-diffusion-2-1's 866M parameters, from other model lines.
- dreamshaper-7 (860M, F32)
- LCM_Dreamshaper_v7 (860M, F32)
- Photon_v1 (860M, F32)
- Counterfeit-V2.5 (860M, F32)
More on stable-diffusion-2-1
Fits on an 8 GB GPU at FP32: every model that fits in 8 GB.
Related reading: V100 pricing and specs, Fast diffusion inference on GPU VMs, What AI inference is, and What AI model fine-tuning is.
