text model · Ornith · iOS
Can I run Ornith 1.0 35B on iPhone Air?
No. Ornith 1.0 35B needs ~23.2 GB even at Q4_K_M, but iPhone Air only has ~8 GB usable.
Needs ~23.2 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 15.2 GB: Ornith 1.0 35B needs roughly 23.2 GB at Q4_K_M and iPhone Air leaves only about 8 GB usable for a model. The lightest tracked hardware that runs Ornith 1.0 35B is the Nvidia GeForce RTX 5090 (32GB) at 32 GB. See Ornith 1.0 35B on Nvidia GeForce RTX 5090 (32GB).
- Q4_K_M needed
- ~23.2 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
- 35B (MoE, 3B active)
- Q4_K_M size
- 21 GB
- Q8_0 size
- 37 GB
- Context
- 256k
- Ollama tag
- ornith:35b
- Memory
- 12 GB unified
- Usable for weights
- ~8 GB
- Best runtime
- llama.cpp + Metal (via PocketPal or Off Grid app)
What you can run instead
Run Ornith 1.0 35B on other hardware
FAQ
Can iPhone Air run Ornith 1.0 35B?
No. Ornith 1.0 35B needs ~23.2 GB even at Q4_K_M, but iPhone Air only has ~8 GB usable.
How much memory does Ornith 1.0 35B need?
iPhone Air does not have enough memory. At Q4_K_M the weights are ~21 GB; with KV cache and runtime overhead, budget ~23.2 GB at a 4k context. It is a Mixture-of-Experts model (35B total / 3B active), so all experts must stay in memory; memory tracks total params, not active params.
What is the best tool to run Ornith 1.0 35B 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
[](https://localmodel.run/can-i-run/ornith-1.0-35b/iphone-air) 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
- gsmarena.com
- huggingface.co/deepreinforce-ai/Ornith-1.0-35B
- huggingface.co/deepreinforce-ai/Ornith-1.0-35B-GGUF
- layla-network.ai
- macrumors.com
- ollama.com
- 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.