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text model · Mistral · iOS

Can I run Ministral 3 8B on iPhone 15 Pro?

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

No. Ministral 3 8B needs ~6.3 GB even at Q4_K_M, but iPhone 15 Pro only has ~4.5 GB usable.

Needs ~6.3 GB Device usable ~4.5 GB

Needs ~6.3 GB even at Q4_K_M, but only ~4.5 GB is usable.

That figure is at a 4k context and moves about ±15% as context length changes.

The gap is about 1.8 GB: Ministral 3 8B needs roughly 6.3 GB at Q4_K_M and iPhone 15 Pro leaves only about 4.5 GB usable for a model. The lightest tracked hardware that runs Ministral 3 8B is the Nvidia GeForce RTX 3060 Ti (8GB) at 8 GB. See Ministral 3 8B on Nvidia GeForce RTX 3060 Ti (8GB).

Q4_K_M needed
~6.3 GB
Usable on device
~4.5 GB
Device memory
8 GB
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Which quant fits

Quant ladder vs ~4.5 GB usable
Q2_K
~4.9 GB
Q3_K_M
~5.4 GB
Q4_K_M
~6.3 GB
Q5_K_M
~7.2 GB
Q6_K
~8.1 GB
Q8_0
~9.9 GB
FP16
~19.4 GB
The line marks iPhone 15 Pro's ~4.5 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 Mistral
Parameters
8B
Q4_K_M size
4.84 GB
Q8_0 size
8.41 GB
Context
256k
Ollama tag
ministral-3:8b
Full Ministral 3 8B requirements →
Device iOS
Memory
8 GB unified
Usable for weights
~4.5 GB
Power draw
~14 W
Best runtime
llama.cpp + Metal (via PocketPal or Off Grid app)
Best models for iPhone 15 Pro →

What you can run instead

Run Ministral 3 8B on other hardware

FAQ

Can iPhone 15 Pro run Ministral 3 8B?

No. Ministral 3 8B needs ~6.3 GB even at Q4_K_M, but iPhone 15 Pro only has ~4.5 GB usable.

How much memory does Ministral 3 8B need?

iPhone 15 Pro does not have enough memory. At Q4_K_M the weights are ~4.84 GB; with KV cache and runtime overhead, budget ~6.3 GB at a 4k context.

What is the best tool to run Ministral 3 8B 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.