text model · Ornith · iOS
Can I run Ornith 1.0 9B on iPhone 17 Pro?
Yes. Ornith 1.0 9B runs on iPhone 17 Pro at Q4_K_M (~7.1 GB of ~8 GB usable).
Fits at Q4_K_M (~7.1 GB of ~8 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 17 Pro leaves ~0.9 GB of headroom.
- Q4_K_M needed
- ~7.1 GB
- Usable on device
- ~8 GB
- Device memory
- 12 GB
- Best quant
- Q4_K_M
Which quant fits
Running cost · estimate
- Power draw
- ~12 W
- Electricity / 1M tokens
- ~$0.07
- Pays for itself after
- ~2,556M tok
At ~$0.15/kWh and the estimated ~7 tok/s, a million generated tokens costs about $0.07 in electricity. Versus a hosted API at ~$0.5 per million tokens, the ~$1,099 iPhone 17 Pro pays for itself after roughly 2,556 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
- 9B
- Q4_K_M size
- 5.6 GB
- Q8_0 size
- 9.5 GB
- Context
- 256k
- Ollama tag
- ornith:9b
- Memory
- 12 GB unified
- Usable for weights
- ~8 GB
- Power draw
- ~12 W
- Best runtime
- llama.cpp + Metal (via PocketPal or Off Grid app)
You could also run
Run Ornith 1.0 9B on other hardware
FAQ
Can iPhone 17 Pro run Ornith 1.0 9B?
Yes. Ornith 1.0 9B runs on iPhone 17 Pro at Q4_K_M (~7.1 GB of ~8 GB usable).
How much memory does Ornith 1.0 9B need?
It is a tight fit on iPhone 17 Pro. At Q4_K_M the weights are ~5.6 GB; with KV cache and runtime overhead, budget ~7.1 GB at a 4k context.
What is the best tool to run Ornith 1.0 9B 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/ornith-1.0-9b/iphone-17-pro) Sources
- apple.com
- deep-reinforce.com
- developer.apple.com
- en.wikipedia.org
- enclaveai.app
- github.com/a-ghorbani
- github.com/google-ai-edge
- github.com/mainframecomputer
- huggingface.co/deepreinforce-ai/Ornith-1.0-9B
- huggingface.co/deepreinforce-ai/Ornith-1.0-9B-GGUF
- layla-network.ai
- macrumors.com
- ollama.com
- 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.