text model · Phi-4 · Android
Can I run Phi-4-mini-reasoning on Generic Android Phone (8GB RAM)?
Yes. Phi-4-mini-reasoning runs on Generic Android Phone (8GB RAM) at Q4_K_M (~3.6 GB of ~4.5 GB usable).
Fits at Q4_K_M (~3.6 GB of ~4.5 GB usable) but with little headroom. Close background apps, and expect slow generation on a phone.
That figure is at a 4k context and moves about ±15% as context length changes. Generic Android Phone (8GB RAM) leaves ~0.9 GB of headroom.
- Q4_K_M needed
- ~3.6 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
- 3.8B
- Q4_K_M size
- 2.32 GB
- Q8_0 size
- 3.8 GB
- Context
- 128k
- Ollama tag
- phi4-mini-reasoning:latest
- Memory
- 8 GB ram
- Usable for weights
- ~4.5 GB
- Best runtime
- llama.cpp (PocketPal or SmolChat)
You could also run
Run Phi-4-mini-reasoning on other hardware
FAQ
Can Generic Android Phone (8GB RAM) run Phi-4-mini-reasoning?
Yes. Phi-4-mini-reasoning runs on Generic Android Phone (8GB RAM) at Q4_K_M (~3.6 GB of ~4.5 GB usable).
How much memory does Phi-4-mini-reasoning need?
It is a tight fit on Generic Android Phone (8GB RAM). At Q4_K_M the weights are ~2.32 GB; with KV cache and runtime overhead, budget ~3.6 GB at a 4k context.
What is the best tool to run Phi-4-mini-reasoning 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/phi-4-mini-reasoning/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.