text model · Llama 3.2 Vision · iOS
Can I run Llama 3.2 Vision 11B on iPhone 17 Pro?
No. Llama 3.2 Vision 11B needs ~9 GB even at Q4_K_M, but iPhone 17 Pro only has ~8 GB usable.
Needs ~9 GB even at Q4_K_M, but only ~8 GB is usable.
That figure is at a 4k context and moves about ±15% as context length changes.
The gap is about 1 GB: Llama 3.2 Vision 11B needs roughly 9 GB at Q4_K_M and iPhone 17 Pro leaves only about 8 GB usable for a model. The lightest tracked hardware that runs Llama 3.2 Vision 11B is the Nvidia GeForce RTX 3060 (12GB) at 12 GB. See Llama 3.2 Vision 11B on Nvidia GeForce RTX 3060 (12GB).
- Q4_K_M needed
- ~9 GB
- Usable on device
- ~8 GB
- Device memory
- 12 GB
Which quant fits
How to run it
On iOS use Apple Foundation Models (Built into iOS 26, ~3B on-device model, zero download, fully private.).
- Parameters
- 10.7B
- Q4_K_M size
- 7.36 GB
- Q8_0 size
- 11.49 GB
- Context
- 128k
- Ollama tag
- llama3.2-vision:11b
- Memory
- 12 GB unified
- Usable for weights
- ~8 GB
- Power draw
- ~12 W
- Best runtime
- llama.cpp + Metal (via PocketPal or Off Grid app)
What you can run instead
Run Llama 3.2 Vision 11B on other hardware
FAQ
Can iPhone 17 Pro run Llama 3.2 Vision 11B?
No. Llama 3.2 Vision 11B needs ~9 GB even at Q4_K_M, but iPhone 17 Pro only has ~8 GB usable.
How much memory does Llama 3.2 Vision 11B need?
iPhone 17 Pro does not have enough memory. At Q4_K_M the weights are ~7.36 GB; with KV cache and runtime overhead, budget ~9 GB at a 4k context.
What is the best tool to run Llama 3.2 Vision 11B on iOS?
On iPhone and iPad, Apple Foundation Models (Built into iOS 26, ~3B on-device model, zero download, fully private.) is the standard choice. Phones realistically run 1B-4B class models. Anything larger thermally throttles or OOMs.
Embed this
A live badge for your model card or README, updated as the data is. [](https://localmodel.run/can-i-run/llama-3.2-vision-11b/iphone-17-pro) Sources
- apple.com
- developer.apple.com
- en.wikipedia.org
- enclaveai.app
- github.com/a-ghorbani
- github.com/google-ai-edge
- github.com/mainframecomputer
- huggingface.co/leafspark
- huggingface.co/meta-llama
- layla-network.ai
- macrumors.com
- ollama.com/library/llama3.2-vision
- ollama.com/library/llama3.2-vision/tags
- privatellm.app
- wccftech.com
Weights are measured from GGUF files; KV cache and overhead are computed, so totals can vary ~15% with context and runtime. Any tok/s is a bandwidth estimate. See methodology.