text model · Phi-4 · iOS
Can I run Phi-4-mini-reasoning on iPhone 16 Pro?
Yes. Phi-4-mini-reasoning runs on iPhone 16 Pro at Q4_K_M (~3.6 GB of ~4.5 GB usable).
Fits at Q4_K_M (~3.6 GB of ~4.5 GB usable) but with little headroom. Close background apps, and expect slow generation on a phone.
That figure is at a 4k context and moves about ±15% as context length changes. iPhone 16 Pro leaves ~0.9 GB of headroom.
- Q4_K_M needed
- ~3.6 GB
- Usable on device
- ~4.5 GB
- Device memory
- 8 GB
- Best quant
- Q4_K_M
Which quant fits
Running cost · estimate
- Power draw
- ~12 W
- Electricity / 1M tokens
- ~$0.04
- Pays for itself after
- ~2,172M tok
At ~$0.15/kWh and the estimated ~13 tok/s, a million generated tokens costs about $0.04 in electricity. Versus a hosted API at ~$0.5 per million tokens, the ~$999 iPhone 16 Pro pays for itself after roughly 2,172 million tokens, so local hardware is mostly a fixed cost, not a per-token one. TDP is the peak draw, so this is an upper bound. Assumptions.
How to run it
On iOS use Apple Foundation Models (Built into iOS 26, ~3B on-device model, zero download, fully private.).
- Parameters
- 3.8B
- Q4_K_M size
- 2.32 GB
- Q8_0 size
- 3.8 GB
- Context
- 128k
- Ollama tag
- phi4-mini-reasoning:latest
- Memory
- 8 GB unified
- Usable for weights
- ~4.5 GB
- Power draw
- ~12 W
- Best runtime
- llama.cpp + Metal (via PocketPal or Off Grid app)
You could also run
Run Phi-4-mini-reasoning on other hardware
FAQ
Can iPhone 16 Pro run Phi-4-mini-reasoning?
Yes. Phi-4-mini-reasoning runs on iPhone 16 Pro at Q4_K_M (~3.6 GB of ~4.5 GB usable).
How much memory does Phi-4-mini-reasoning need?
It is a tight fit on iPhone 16 Pro. At Q4_K_M the weights are ~2.32 GB; with KV cache and runtime overhead, budget ~3.6 GB at a 4k context.
What is the best tool to run Phi-4-mini-reasoning 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/phi-4-mini-reasoning/iphone-16-pro) Sources
- abachy.com
- apple.com/iphone-16-pro
- apple.com/newsroom
- developer.apple.com
- en.wikipedia.org
- enclaveai.app
- github.com/a-ghorbani
- github.com/google-ai-edge
- github.com/mainframecomputer
- gsmarena.com
- huggingface.co/microsoft
- huggingface.co/unsloth
- layla-network.ai
- macrumors.com
- ollama.com
- privatellm.app
- versus.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.