text model · Command R · iOS
Can I run Command R 35B on iPad Pro M4 (16GB, 1TB/2TB config)?
No. Command R 35B needs ~22.3 GB even at Q4_K_M, but iPad Pro M4 (16GB, 1TB/2TB config) only has ~12 GB usable.
Needs ~22.3 GB even at Q4_K_M, but only ~12 GB is usable.
That figure is at a 4k context and moves about ±15% as context length changes.
The gap is about 10.3 GB: Command R 35B needs roughly 22.3 GB at Q4_K_M and iPad Pro M4 (16GB, 1TB/2TB config) leaves only about 12 GB usable for a model. The lightest tracked hardware that runs Command R 35B is the Nvidia GeForce RTX 4090 (24GB) at 24 GB. See Command R 35B on Nvidia GeForce RTX 4090 (24GB).
- Q4_K_M needed
- ~22.3 GB
- Usable on device
- ~12 GB
- Device memory
- 16 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
- Q4_K_M size
- 20.05 GB
- Q8_0 size
- 34.63 GB
- Context
- 128k
- Ollama tag
- command-r:35b
- Memory
- 16 GB unified
- Usable for weights
- ~12 GB
- Power draw
- ~14 W
- Best runtime
- MLX (via Python or Swift; mlx-lm package)
What you can run instead
Run Command R 35B on other hardware
FAQ
Can iPad Pro M4 (16GB, 1TB/2TB config) run Command R 35B?
No. Command R 35B needs ~22.3 GB even at Q4_K_M, but iPad Pro M4 (16GB, 1TB/2TB config) only has ~12 GB usable.
How much memory does Command R 35B need?
iPad Pro M4 (16GB, 1TB/2TB config) does not have enough memory. At Q4_K_M the weights are ~20.05 GB; with KV cache and runtime overhead, budget ~22.3 GB at a 4k context.
What is the best tool to run Command R 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/command-r-35b/ipad-pro-m4-16gb) Sources
- apple.com/ipad-pro
- apple.com/newsroom
- developer.apple.com
- enclaveai.app
- github.com/a-ghorbani
- github.com/google-ai-edge
- github.com/mainframecomputer
- gsmarena.com
- huggingface.co/bartowski
- huggingface.co/CohereForAI
- layla-network.ai
- ollama.com/library/command-r
- ollama.com/library/command-r/tags
- phonearena.com
- privatellm.app
- support.apple.com/en-us/119891
- support.apple.com/en-us/119892
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.