image model · stable-diffusion · iOS
Can I run Stable Diffusion 3.5 Large on iPhone 17?
Needs ~7 GB at Q4 GGUF, but only ~4.5 GB is usable on iPhone 17. With aggressive CPU offload it can run on as little as ~5 GB, much slower.
Needs ~7 GB at Q4 GGUF, but only ~4.5 GB is usable on iPhone 17. With aggressive CPU offload it can run on as little as ~5 GB, much slower.
The gap is about 2.5 GB: Stable Diffusion 3.5 Large needs roughly 7 GB and iPhone 17 leaves only about 4.5 GB usable for a model. The lightest tracked hardware that runs Stable Diffusion 3.5 Large is the Nvidia GeForce RTX 3060 (12GB) at 12 GB. See Stable Diffusion 3.5 Large on Nvidia GeForce RTX 3060 (12GB).
- Peak VRAM
- ~7 GB
- Usable on device
- ~4.5 GB
- Device memory
- 8 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
- 8 GB unified
- Usable for weights
- ~4.5 GB
- Best runtime
- llama.cpp + Metal (via PocketPal or Off Grid app)
What you can run instead
Run Stable Diffusion 3.5 Large on other hardware
FAQ
Can iPhone 17 run Stable Diffusion 3.5 Large?
Needs ~7 GB at Q4 GGUF, but only ~4.5 GB is usable on iPhone 17. 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?
iPhone 17 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
- apple.com
- developer.apple.com
- en.wikipedia.org/wiki/Apple_A19
- en.wikipedia.org/wiki/IPhone_17
- enclaveai.app
- github.com/a-ghorbani
- github.com/google-ai-edge
- github.com/mainframecomputer
- huggingface.co/city96
- huggingface.co/docs
- layla-network.ai
- macrumors.com
- privatellm.app
- 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
VRAM figures are sourced peak-usage anchors at the noted quant, validated 2026-08-03. See methodology.