text model · EXAONE · Android

Can I run EXAONE 4.0 32B on Google Pixel 9 Pro?

Compatibility verdict VRAM threshold engine

No, not enough memory would not load

No. EXAONE 4.0 32B needs ~20.2 GB even at Q4_K_M, but Google Pixel 9 Pro only has ~10.5 GB usable.

Needs ~20.2 GB Device usable ~10.5 GB

Needs ~20.2 GB even at Q4_K_M, but only ~10.5 GB is usable.

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

The gap is about 9.7 GB: EXAONE 4.0 32B needs roughly 20.2 GB at Q4_K_M and Google Pixel 9 Pro leaves only about 10.5 GB usable for a model. The lightest tracked hardware that runs EXAONE 4.0 32B is the Nvidia GeForce RTX 4090 (24GB) at 24 GB. See EXAONE 4.0 32B on Nvidia GeForce RTX 4090 (24GB).

Q4_K_M needed: ~20.2 GB
Usable on device: ~10.5 GB
Device memory: 16 GB

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Which quant fits

Quant ladder vs ~10.5 GB usable

Q2_K

~15.6 GB

Q3_K_M

~17.8 GB

Q4_K_M

~20.2 GB

Q5_K_M

~25 GB

Q6_K

~28.4 GB

Q8_0

~33.9 GB

FP16

~66.2 GB

The line marks Google Pixel 9 Pro's ~10.5 GB budget; rungs past it are too large.

How to run it

On Android use PocketPal AI (Polished app, download GGUF and run offline.).

Model EXAONE

Parameters: 32B
Q4_K_M size: 18.02 GB
Q8_0 size: 31.67 GB
Context: 131k

Full EXAONE 4.0 32B requirements →

Device Android

Memory: 16 GB ram
Usable for weights: ~10.5 GB
Best runtime: llama.cpp (PocketPal) or MLC-LLM (Adreno GPU path)

Best models for Google Pixel 9 Pro →

What you can run instead

Run EXAONE 4.0 32B 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 M5 (32GB)

FAQ

Can Google Pixel 9 Pro run EXAONE 4.0 32B?

No. EXAONE 4.0 32B needs ~20.2 GB even at Q4_K_M, but Google Pixel 9 Pro only has ~10.5 GB usable.

How much memory does EXAONE 4.0 32B need?

Google Pixel 9 Pro does not have enough memory. At Q4_K_M the weights are ~18.02 GB; with KV cache and runtime overhead, budget ~20.2 GB at a 4k context.

What is the best tool to run EXAONE 4.0 32B on Android?

On Android, PocketPal AI (Polished app, download GGUF and run offline.) is the go-to option. NPU acceleration is limited and chip-specific; most apps run on CPU. Expect 1B-4B class.

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Markdown

[![EXAONE 4.0 32B on Google Pixel 9 Pro](https://localmodel.run/badge/exaone-4-32b/pixel-9-pro.svg)](https://localmodel.run/can-i-run/exaone-4-32b/pixel-9-pro)

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.