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text model · Olmo · iOS

Can I run Olmo 3.1 32B Instruct on iPhone 15 Pro?

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

No. Olmo 3.1 32B Instruct needs ~20.3 GB even at Q4_K_M, but iPhone 15 Pro only has ~4.5 GB usable.

Needs ~20.3 GB Device usable ~4.5 GB

Needs ~20.3 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 15.8 GB: Olmo 3.1 32B Instruct needs roughly 20.3 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 Olmo 3.1 32B Instruct is the Nvidia GeForce RTX 4090 (24GB) at 24 GB. See Olmo 3.1 32B Instruct on Nvidia GeForce RTX 4090 (24GB).

Q4_K_M needed
~20.3 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
~15.6 GB
Q3_K_M
~17.8 GB
Q4_K_M
~20.3 GB
Q5_K_M
~25 GB
Q6_K
~28.4 GB
Q8_0
~34.1 GB
FP16
~66.2 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 Olmo
Parameters
32B
Q4_K_M size
18.14 GB
Q8_0 size
31.9 GB
Context
64k
Ollama tag
olmo-3.1:32b-instruct
Full Olmo 3.1 32B Instruct 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 Olmo 3.1 32B Instruct on other hardware

FAQ

Can iPhone 15 Pro run Olmo 3.1 32B Instruct?

No. Olmo 3.1 32B Instruct needs ~20.3 GB even at Q4_K_M, but iPhone 15 Pro only has ~4.5 GB usable.

How much memory does Olmo 3.1 32B Instruct need?

iPhone 15 Pro does not have enough memory. At Q4_K_M the weights are ~18.14 GB; with KV cache and runtime overhead, budget ~20.3 GB at a 4k context.

What is the best tool to run Olmo 3.1 32B Instruct 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.