text model · Gemma · iOS

Can I run Gemma 3n E4B on iPhone 16?

Compatibility verdict VRAM threshold engine

No, not enough memory would not load

No. Gemma 3n E4B needs ~5.7 GB even at Q4_K_M, but iPhone 16 only has ~4.5 GB usable.

Needs ~5.7 GB Device usable ~4.5 GB

Needs ~5.7 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 1.2 GB: Gemma 3n E4B needs roughly 5.7 GB at Q4_K_M and iPhone 16 leaves only about 4.5 GB usable for a model. The lightest tracked hardware that runs Gemma 3n E4B is the Nvidia GeForce RTX 3060 (12GB) at 12 GB. See Gemma 3n E4B on Nvidia GeForce RTX 3060 (12GB).

Q4_K_M needed: ~5.7 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

~4.9 GB

Q3_K_M

~5.4 GB

Q4_K_M

~5.7 GB

Q5_K_M

~7.2 GB

Q6_K

~8.1 GB

Q8_0

~8.4 GB

FP16

~15.2 GB

The line marks iPhone 16'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 Gemma

Parameters: 8B
Q4_K_M size: 4.23 GB
Q8_0 size: 6.85 GB
Context: 32k
Ollama tag: gemma3n:e4b

Full Gemma 3n E4B requirements →

Device iOS

Memory: 8 GB unified
Usable for weights: ~4.5 GB
Power draw: ~11 W
Best runtime: llama.cpp + Metal (via PocketPal or Off Grid app)

Best models for iPhone 16 →

What you can run instead

Run Gemma 3n E4B on other hardware

Nvidia GeForce RTX 3060 (12GB)Nvidia GeForce RTX 4070 (12GB)Samsung Galaxy S24 Ultra Generic Android Phone (12GB RAM)iPhone 17 Pro iPhone Air

FAQ

Can iPhone 16 run Gemma 3n E4B?

No. Gemma 3n E4B needs ~5.7 GB even at Q4_K_M, but iPhone 16 only has ~4.5 GB usable.

How much memory does Gemma 3n E4B need?

iPhone 16 does not have enough memory. At Q4_K_M the weights are ~4.23 GB; with KV cache and runtime overhead, budget ~5.7 GB at a 4k context.

What is the best tool to run Gemma 3n E4B 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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Markdown

[![Gemma 3n E4B on iPhone 16](https://localmodel.run/badge/gemma-3n-e4b/iphone-16.svg)](https://localmodel.run/can-i-run/gemma-3n-e4b/iphone-16)

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.