text model · Gemma · Android
Can I run Gemma 3 12B on Generic Android Phone (8GB RAM)?
No. Gemma 3 12B needs ~8.9 GB even at Q4_K_M, but Generic Android Phone (8GB RAM) only has ~4.5 GB usable.
Needs ~8.9 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 4.4 GB: Gemma 3 12B needs roughly 8.9 GB at Q4_K_M and Generic Android Phone (8GB RAM) leaves only about 4.5 GB usable for a model. The lightest tracked hardware that runs Gemma 3 12B is the Nvidia GeForce RTX 3060 (12GB) at 12 GB. See Gemma 3 12B on Nvidia GeForce RTX 3060 (12GB).
- Q4_K_M needed
- ~8.9 GB
- Usable on device
- ~4.5 GB
- Device memory
- 8 GB
Which quant fits
How to run it
On Android use PocketPal AI (Polished app, download GGUF and run offline.).
- Parameters
- 12B
- Q4_K_M size
- 7.3 GB
- Q8_0 size
- 12.51 GB
- Context
- 128k
- Ollama tag
- gemma3:12b
- Memory
- 8 GB ram
- Usable for weights
- ~4.5 GB
- Best runtime
- llama.cpp (PocketPal or SmolChat)
What you can run instead
Run Gemma 3 12B on other hardware
FAQ
Can Generic Android Phone (8GB RAM) run Gemma 3 12B?
No. Gemma 3 12B needs ~8.9 GB even at Q4_K_M, but Generic Android Phone (8GB RAM) only has ~4.5 GB usable.
How much memory does Gemma 3 12B need?
Generic Android Phone (8GB RAM) does not have enough memory. At Q4_K_M the weights are ~7.3 GB; with KV cache and runtime overhead, budget ~8.9 GB at a 4k context.
What is the best tool to run Gemma 3 12B 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.
Embed this
A live badge for your model card or README, updated as the data is. [](https://localmodel.run/can-i-run/gemma-3-12b/android-generic-8gb) Sources
- github.com/a-ghorbani
- github.com/google-ai-edge
- github.com/mlc-ai
- github.com/shubham0204
- github.com/Vali-98
- gorilla.cs.berkeley.edu
- huggingface.co/bartowski/google_gemma-3-12b-it-GGUF
- huggingface.co/bartowski/Llama-3.2-1B-Instruct-GGUF
- layla-network.ai
- lmarena.ai
- ollama.com/library/gemma3
- ollama.com/library/gemma3/tags
- ollama.com/library/llama3.2:1b
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.