text model · phi · Android
Can I run Phi-4-mini 3.8B on Generic Android Phone (8GB RAM)?
Yes. Phi-4-mini 3.8B 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
- 3.8B
- Q4_K_M size
- 2.49 GB
- Q8_0 size
- 4.08 GB
- Context
- 128k
- Ollama tag
- phi4-mini:3.8b
- 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 3.8B on other hardware
FAQ
Can Generic Android Phone (8GB RAM) run Phi-4-mini 3.8B?
Yes. Phi-4-mini 3.8B runs on Generic Android Phone (8GB RAM) at Q4_K_M (~3.8 GB of ~4.5 GB usable).
How much memory does Phi-4-mini 3.8B need?
It is a tight fit on Generic Android Phone (8GB RAM). At Q4_K_M the weights are ~2.49 GB; with KV cache and runtime overhead, budget ~3.8 GB at a 4k context.
What is the best tool to run Phi-4-mini 3.8B 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.