text model · Qwen3 · Android
Can I run Qwen3 30B-A3B on Generic Android Phone (8GB RAM)?
No. Qwen3 30B-A3B needs ~20.7 GB even at Q4_K_M, but Generic Android Phone (8GB RAM) only has ~4.5 GB usable.
Needs ~20.7 GB even at Q4_K_M, but only ~4.5 GB is usable.
- Q4_K_M needed
- ~20.7 GB
- Usable on device
- ~4.5 GB
- Device memory
- 8 GB
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
- 30.5B (MoE, 3.3B active)
- Q4_K_M size
- 18.6 GB
- Q8_0 size
- 32.5 GB
- Context
- 32k
- Ollama tag
- qwen3:30b-a3b
- Memory
- 8 GB ram
- Usable for weights
- ~4.5 GB
- Best runtime
- llama.cpp (PocketPal or SmolChat)
What you can run instead
Run Qwen3 30B-A3B on other hardware
FAQ
Can Generic Android Phone (8GB RAM) run Qwen3 30B-A3B?
No. Qwen3 30B-A3B needs ~20.7 GB even at Q4_K_M, but Generic Android Phone (8GB RAM) only has ~4.5 GB usable.
How much memory does Qwen3 30B-A3B need?
Generic Android Phone (8GB RAM) does not have enough memory. At Q4_K_M the weights are ~18.6 GB; with KV cache and runtime overhead, budget ~20.7 GB at a 4k context. It is a Mixture-of-Experts model (30.5B total / 3.3B active), so all experts must stay in memory; memory tracks total params, not active params.
What is the best tool to run Qwen3 30B-A3B 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.