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text model · gpt-oss · iOS

Can I run gpt-oss 20B on iPhone 17?

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

No. gpt-oss 20B needs ~13.2 GB even at Q4_K_M, but iPhone 17 only has ~4.5 GB usable.

Needs ~13.2 GB Device usable ~4.5 GB

Needs ~13.2 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 8.7 GB: gpt-oss 20B needs roughly 13.2 GB at Q4_K_M and iPhone 17 leaves only about 4.5 GB usable for a model. The lightest tracked hardware that runs gpt-oss 20B is the Nvidia GeForce RTX 4060 Ti (16GB) at 16 GB. See gpt-oss 20B on Nvidia GeForce RTX 4060 Ti (16GB).

Q4_K_M needed
~13.2 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
~10.7 GB
Q3_K_M
~12.2 GB
Q4_K_M
~13.2 GB
Q5_K_M
~16.9 GB
Q6_K
~19.1 GB
Q8_0
~2.8 GB
FP16
~43.9 GB
The line marks iPhone 17'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 gpt-oss
Parameters
21B (MoE, 3.6B active)
Q4_K_M size
11.28 GB
Q8_0 size
0.86 GB
Context
128k
Ollama tag
gpt-oss:20b
Full gpt-oss 20B requirements →
Device iOS
Memory
8 GB unified
Usable for weights
~4.5 GB
Best runtime
llama.cpp + Metal (via PocketPal or Off Grid app)
Best models for iPhone 17 →

What you can run instead

Run gpt-oss 20B on other hardware

FAQ

Can iPhone 17 run gpt-oss 20B?

No. gpt-oss 20B needs ~13.2 GB even at Q4_K_M, but iPhone 17 only has ~4.5 GB usable.

How much memory does gpt-oss 20B need?

iPhone 17 does not have enough memory. At Q4_K_M the weights are ~11.28 GB; with KV cache and runtime overhead, budget ~13.2 GB at a 4k context. It is a Mixture-of-Experts model (21B total / 3.6B active), so all experts must stay in memory; memory tracks total params, not active params.

What is the best tool to run gpt-oss 20B 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.