text model · Gemma · Android

Can I run Gemma 3 27B on Google Pixel 9 Pro?

Compatibility verdict VRAM threshold engine

No, not enough memory would not load

No. Gemma 3 27B needs ~18.6 GB even at Q4_K_M, but Google Pixel 9 Pro only has ~10.5 GB usable.

Needs ~18.6 GB Device usable ~10.5 GB

Needs ~18.6 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 8.1 GB: Gemma 3 27B needs roughly 18.6 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 Gemma 3 27B is the Nvidia GeForce RTX 4090 (24GB) at 24 GB. See Gemma 3 27B on Nvidia GeForce RTX 4090 (24GB).

Q4_K_M needed: ~18.6 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

~13.3 GB

Q3_K_M

~15.2 GB

Q4_K_M

~18.6 GB

Q5_K_M

~21.2 GB

Q6_K

~24.1 GB

Q8_0

~30.7 GB

FP16

~56 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 Gemma

Parameters: 27B
Q4_K_M size: 16.55 GB
Q8_0 size: 28.71 GB
Context: 128k
Ollama tag: gemma3:27b

Full Gemma 3 27B 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 Gemma 3 27B 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 Gemma 3 27B?

No. Gemma 3 27B needs ~18.6 GB even at Q4_K_M, but Google Pixel 9 Pro only has ~10.5 GB usable.

How much memory does Gemma 3 27B need?

Google Pixel 9 Pro does not have enough memory. At Q4_K_M the weights are ~16.55 GB; with KV cache and runtime overhead, budget ~18.6 GB at a 4k context.

What is the best tool to run Gemma 3 27B 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

[![Gemma 3 27B on Google Pixel 9 Pro](https://localmodel.run/badge/gemma-3-27b/pixel-9-pro.svg)](https://localmodel.run/can-i-run/gemma-3-27b/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.