Stable Diffusion · Stability AI

Stable Diffusion 3.5 Large

See recommended builds Can my GPU run it?

Hardware requirement

Estimated

Running Stable Diffusion 3.5 Large as FP8 at ~8,192 tokens of context.

VRAM to run on GPU

10 GB

10 GB class card

System RAM

16 GB

recommended

Storage

2 TB

NVMe recommended

GPU importance

Critical

memory-bound workload

How this is calculated

Model weights derived	8.1 GB	8.1 billion parameters × 1.00 bytes/param (FP8) ≈ 8.1 GB.
Runtime overhead estimate	1.5 GB	Fixed VRAM overhead for activations and framework context.
Total VRAM to run fully on GPU estimate	9.6 GB	Weights + KV cache + overhead. This is what you need to keep the whole model on the graphics card for full speed.
Recommended system RAM estimate	16 GB	Headroom to load the model, run the OS, and hold any layers offloaded from the GPU. Rounded up to a standard kit size.
Storage estimate	2 TB NVMe recommended	Local model files are large (often tens of GB each). An NVMe SSD keeps load times reasonable.

Memory figures are estimates derived from published model facts using a fixed method. They are not benchmarks. We do not publish tokens-per-second figures because we have no measured source for them.

This model does not expose transformer attention dimensions (typical for diffusion image models), so context/KV-cache memory is not modelled. The estimate covers weights plus runtime overhead only.

Quantizations

Smaller quantizations use less memory with some quality trade-off. The default below balances size and quality.

Variant	Bytes / param	Note
FP8 default	1	Half the memory of FP16 with minor quality trade-off; common for consumer GPUs.
FP16	2	Full precision weights; best quality, needs more VRAM.

Architecture facts sourced from the model creator's published configuration: https://huggingface.co/stabilityai.