image model · stable-diffusion · Windows
Can I run Stable Diffusion 3.5 Large on Nvidia GeForce RTX 2060 (6GB)?
Needs ~7 GB at Q4 GGUF, but only ~5 GB is usable on Nvidia GeForce RTX 2060 (6GB). With aggressive CPU offload it can run on as little as ~5 GB, much slower.
Needs ~7 GB at Q4 GGUF, but only ~5 GB is usable on Nvidia GeForce RTX 2060 (6GB). With aggressive CPU offload it can run on as little as ~5 GB, much slower.
The gap is about 2 GB: Stable Diffusion 3.5 Large needs roughly 7 GB and Nvidia GeForce RTX 2060 (6GB) leaves only about 5 GB usable for a model. The lightest tracked hardware that runs Stable Diffusion 3.5 Large is the Nvidia GeForce RTX 3060 Ti (8GB) at 8 GB. See Stable Diffusion 3.5 Large on Nvidia GeForce RTX 3060 Ti (8GB).
- Peak VRAM
- ~7 GB
- Usable on device
- ~5 GB
- Device memory
- 6 GB
- Quant
- Q4 GGUF
- Type
- image (MMDIT)
- Parameters
- 8.1B
- Peak VRAM
- ~7 GB at Q4 GGUF
- Resolution
- 1024×1024
- License
- Stability Community License
- Memory
- 6 GB vram
- Usable for weights
- ~5 GB
- Power draw
- ~160 W
- Best runtime
- Ollama (CUDA) / llama.cpp CUDA
What you can run instead
Run Stable Diffusion 3.5 Large on other hardware
FAQ
Can Nvidia GeForce RTX 2060 (6GB) run Stable Diffusion 3.5 Large?
Needs ~7 GB at Q4 GGUF, but only ~5 GB is usable on Nvidia GeForce RTX 2060 (6GB). With aggressive CPU offload it can run on as little as ~5 GB, much slower.
How much VRAM does Stable Diffusion 3.5 Large need?
Nvidia GeForce RTX 2060 (6GB) does not have enough memory. At Q4 GGUF the realistic peak is ~7 GB of VRAM, versus ~19 GB with every component kept resident (no offload). With aggressive CPU offload it drops to ~5 GB, much slower.
What do I use to run Stable Diffusion 3.5 Large locally?
Stable Diffusion 3.5 Large runs in ComfyUI or Draw Things (among others). It loads as a diffusion checkpoint plus its text encoder and VAE, not a single chat command.
Sources
- en.wikipedia.org
- huggingface.co/city96
- huggingface.co/docs
- lmstudio.ai
- ollama.com
- stability.ai/news-updates/introducing-stable-diffusion-3-5
- stability.ai/news-updates/stable-diffusion-35-models-optimized-with-tensorrt-deliver-2x-faster-performance-and-40-less-memory-on-nvidia-rtx-gpus
- techspot.com
- videocardz.net
VRAM figures are sourced peak-usage anchors at the noted quant, validated 2026-09-14. See methodology.