text model · Mistral · iOS
Can I run Magistral Small on iPad Pro M4 (16GB, 1TB/2TB config)?
No. Magistral Small needs ~16 GB even at Q4_K_M, but iPad Pro M4 (16GB, 1TB/2TB config) only has ~12 GB usable.
Needs ~16 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 4 GB: Magistral Small needs roughly 16 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 Magistral Small is the Apple M4 (24GB) at 24 GB. See Magistral Small on Apple M4 (24GB).
- Q4_K_M needed
- ~16 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
- 24B
- Q4_K_M size
- 14 GB
- Q8_0 size
- 25 GB
- Context
- 40k
- Ollama tag
- magistral:24b
- 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 Magistral Small on other hardware
FAQ
Can iPad Pro M4 (16GB, 1TB/2TB config) run Magistral Small?
No. Magistral Small needs ~16 GB even at Q4_K_M, but iPad Pro M4 (16GB, 1TB/2TB config) only has ~12 GB usable.
How much memory does Magistral Small need?
iPad Pro M4 (16GB, 1TB/2TB config) does not have enough memory. At Q4_K_M the weights are ~14 GB; with KV cache and runtime overhead, budget ~16 GB at a 4k context.
What is the best tool to run Magistral Small 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/magistral-small/ipad-pro-m4-16gb) 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.