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Can I run OLMo 2 32B Instruct on iPhone 15 Pro?

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

No. OLMo 2 32B Instruct needs ~21.7 GB even at Q4_K_M, but iPhone 15 Pro only has ~4.5 GB usable.

Needs ~21.7 GB Device usable ~4.5 GB

Needs ~21.7 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 17.2 GB: OLMo 2 32B Instruct needs roughly 21.7 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 2 32B Instruct is the Nvidia GeForce RTX 4090 (24GB) at 24 GB. See OLMo 2 32B Instruct on Nvidia GeForce RTX 4090 (24GB).

Q4_K_M needed
~21.7 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
~21.7 GB
Q5_K_M
~25 GB
Q6_K
~28.4 GB
Q8_0
~36.5 GB
FP16
~66.7 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
19.5 GB
Q8_0 size
34.3 GB
Context
4k
Full OLMo 2 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 2 32B Instruct on other hardware

Nvidia GeForce RTX 4090 (24GB)Nvidia GeForce RTX 3090 (24GB)AMD Radeon RX 7900 XTX (24GB)Nvidia GeForce RTX 5090 (32GB)32GB RAM Laptop (CPU/iGPU only)Apple M4 Pro (48GB)

FAQ

Can iPhone 15 Pro run OLMo 2 32B Instruct?

No. OLMo 2 32B Instruct needs ~21.7 GB even at Q4_K_M, but iPhone 15 Pro only has ~4.5 GB usable.

How much memory does OLMo 2 32B Instruct need?

iPhone 15 Pro does not have enough memory. At Q4_K_M the weights are ~19.5 GB; with KV cache and runtime overhead, budget ~21.7 GB at a 4k context.

What is the best tool to run OLMo 2 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.