text model · Qwen2.5 · Android
Can I run Qwen2.5 3B on Generic Android Phone (8GB RAM)?
Yes. Qwen2.5 3B runs on Generic Android Phone (8GB RAM) at Q4_K_M (~3.3 GB of ~4.5 GB usable).
Runs at Q4_K_M using ~3.3 GB of ~4.5 GB usable.
That figure is at a 4k context and moves about ±15% as context length changes. Generic Android Phone (8GB RAM) leaves ~1.2 GB of headroom.
- Q4_K_M needed
- ~3.3 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
- 3.09B
- Q4_K_M size
- 2.1 GB
- Q8_0 size
- 3.62 GB
- Context
- 128k
- Ollama tag
- qwen2.5:3b
- Memory
- 8 GB ram
- Usable for weights
- ~4.5 GB
- Best runtime
- llama.cpp (PocketPal or SmolChat)
You could also run
Run Qwen2.5 3B on other hardware
FAQ
Can Generic Android Phone (8GB RAM) run Qwen2.5 3B?
Yes. Qwen2.5 3B runs on Generic Android Phone (8GB RAM) at Q4_K_M (~3.3 GB of ~4.5 GB usable).
How much memory does Qwen2.5 3B need?
Generic Android Phone (8GB RAM) has room to spare. At Q4_K_M the weights are ~2.1 GB; with KV cache and runtime overhead, budget ~3.3 GB at a 4k context.
What is the best tool to run Qwen2.5 3B 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/qwen2.5-3b/android-generic-8gb) 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.