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Can I run Command R 35B on iPad Pro M4 (16GB, 1TB/2TB config)?

Compatibility verdict VRAM threshold engine
No, not enough memory would not load

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 Device usable ~12 GB

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
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Which quant fits

Quant ladder vs ~12 GB usable
Q2_K
~16.9 GB
Q3_K_M
~19.3 GB
Q4_K_M
~22.3 GB
Q5_K_M
~27.1 GB
Q6_K
~30.9 GB
Q8_0
~36.8 GB
FP16
~72.2 GB
The line marks iPad Pro M4 (16GB, 1TB/2TB config)'s ~12 GB budget; rungs past it are too large.

How to run it

On iOS use Apple Foundation Models (Built into iOS 26, ~3B on-device model, zero download, fully private.).

Model Command R
Parameters
35B
Q4_K_M size
20.05 GB
Q8_0 size
34.63 GB
Context
128k
Ollama tag
command-r:35b
Full Command R 35B requirements →
Device iOS
Memory
16 GB unified
Usable for weights
~12 GB
Power draw
~14 W
Best runtime
MLX (via Python or Swift; mlx-lm package)
Best models for iPad Pro M4 (16GB, 1TB/2TB config) →

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.

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Sources

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.