text model · Qwen2.5-Coder · iOS
Can I run Qwen2.5 Coder 7B on iPhone 16?
No. Qwen2.5 Coder 7B needs ~5.8 GB even at Q4_K_M, but iPhone 16 only has ~4.5 GB usable.
Needs ~5.8 GB even at Q4_K_M, but only ~4.5 GB is usable.
That figure is at a 4k context and moves about ±15% as context length changes.
The gap is about 1.3 GB: Qwen2.5 Coder 7B needs roughly 5.8 GB at Q4_K_M and iPhone 16 leaves only about 4.5 GB usable for a model. The lightest tracked hardware that runs Qwen2.5 Coder 7B is the Nvidia GeForce RTX 3060 (12GB) at 12 GB. See Qwen2.5 Coder 7B on Nvidia GeForce RTX 3060 (12GB).
- Q4_K_M needed
- ~5.8 GB
- Usable on device
- ~4.5 GB
- Device memory
- 8 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
- 7B
- Q4_K_M size
- 4.36 GB
- Q8_0 size
- 7.54 GB
- Context
- 32k
- Ollama tag
- qwen2.5-coder:7b
- Memory
- 8 GB unified
- Usable for weights
- ~4.5 GB
- Power draw
- ~11 W
- Best runtime
- llama.cpp + Metal (via PocketPal or Off Grid app)
What you can run instead
Run Qwen2.5 Coder 7B on other hardware
FAQ
Can iPhone 16 run Qwen2.5 Coder 7B?
No. Qwen2.5 Coder 7B needs ~5.8 GB even at Q4_K_M, but iPhone 16 only has ~4.5 GB usable.
How much memory does Qwen2.5 Coder 7B need?
iPhone 16 does not have enough memory. At Q4_K_M the weights are ~4.36 GB; with KV cache and runtime overhead, budget ~5.8 GB at a 4k context.
What is the best tool to run Qwen2.5 Coder 7B 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/qwen2.5-coder-7b/iphone-16) Sources
- apple.com
- developer.apple.com
- en.wikipedia.org/wiki/Apple_A18
- en.wikipedia.org/wiki/IPhone_16
- enclaveai.app
- github.com/a-ghorbani
- github.com/google-ai-edge
- github.com/mainframecomputer
- gsmarena.com
- huggingface.co/bartowski
- huggingface.co/Qwen
- layla-network.ai
- macrumors.com
- ollama.com/library/qwen2.5-coder
- ollama.com/library/qwen2.5-coder/tags
- privatellm.app
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.