text model · Qwen3 · Android
Can I run Qwen3 4B on Generic Android Phone (8GB RAM)?
Yes. Qwen3 4B runs on Generic Android Phone (8GB RAM) at Q4_K_M (~3.8 GB of ~4.5 GB usable).
Fits at Q4_K_M (~3.8 GB of ~4.5 GB usable) but with little headroom, close other apps.
- Q4_K_M needed
- ~3.8 GB
- Usable on device
- ~4.5 GB
- Device memory
- 8 GB
- Best quant
- Q4_K_M
How to run it
On Android use PocketPal AI (Polished app, download GGUF and run offline.). NPU acceleration is limited and chip-specific; most apps run on CPU. Expect 1B-4B class.
- Parameters
- 4B
- Q4_K_M size
- 2.5 GB
- Q8_0 size
- 4.28 GB
- Context
- 32k
- Ollama tag
- qwen3:4b
- Memory
- 8 GB ram
- Usable for weights
- ~4.5 GB
- Best runtime
- llama.cpp (PocketPal or SmolChat)
You could also run
Run Qwen3 4B on other hardware
FAQ
Can Generic Android Phone (8GB RAM) run Qwen3 4B?
Yes. Qwen3 4B runs on Generic Android Phone (8GB RAM) at Q4_K_M (~3.8 GB of ~4.5 GB usable).
How much memory does Qwen3 4B need?
It is a tight fit on Generic Android Phone (8GB RAM). At Q4_K_M the weights are ~2.5 GB; with KV cache and runtime overhead, budget ~3.8 GB at a 4k context.
What is the best tool to run Qwen3 4B 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.
Sources
Memory figures are estimates. See methodology.