text model · Gemma · Android
Can I run Gemma 2 2B on Generic Android Phone (8GB RAM)?
Yes. Gemma 2 2B runs on Generic Android Phone (8GB RAM) at Q4_K_M (~2.9 GB of ~4.5 GB usable).
Runs at Q4_K_M using ~2.9 GB of ~4.5 GB usable. You have room for Q8_0 for higher quality.
That figure is at a 4k context and moves about ±15% as context length changes. Generic Android Phone (8GB RAM) leaves ~1.6 GB of headroom, room to step up to Q8_0 for higher quality.
- Q4_K_M needed
- ~2.9 GB
- Usable on device
- ~4.5 GB
- Device memory
- 8 GB
- Best quant
- Q4_K_M
Which quant fits
How to run it
On Android use PocketPal AI (Polished app, download GGUF and run offline.).
- Parameters
- 2.61B
- Q4_K_M size
- 1.71 GB
- Q8_0 size
- 2.78 GB
- Context
- 8k
- Ollama tag
- gemma2:2b
- Memory
- 8 GB ram
- Usable for weights
- ~4.5 GB
- Best runtime
- llama.cpp (PocketPal or SmolChat)
You could also run
Run Gemma 2 2B on other hardware
FAQ
Can Generic Android Phone (8GB RAM) run Gemma 2 2B?
Yes. Gemma 2 2B runs on Generic Android Phone (8GB RAM) at Q4_K_M (~2.9 GB of ~4.5 GB usable).
How much memory does Gemma 2 2B need?
Generic Android Phone (8GB RAM) has room to spare. At Q4_K_M the weights are ~1.71 GB; with KV cache and runtime overhead, budget ~2.9 GB at a 4k context.
What is the best tool to run Gemma 2 2B 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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A live badge for your model card or README, updated as the data is. [](https://localmodel.run/can-i-run/gemma-2-2b/android-generic-8gb) Sources
- github.com/a-ghorbani
- github.com/google-ai-edge
- github.com/mlc-ai
- github.com/shubham0204
- github.com/Vali-98
- huggingface.co/bartowski/gemma-2-2b-it-GGUF
- huggingface.co/bartowski/Llama-3.2-1B-Instruct-GGUF
- huggingface.co/blog/gemma-july-update
- huggingface.co/blog/gemma2
- layla-network.ai
- ollama.com/library/gemma2:2b
- 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.