text model · Qwen3 · Android
Can I run Qwen3 235B A22B on Generic Android Phone (8GB RAM)?
No. Qwen3 235B A22B needs ~136.9 GB even at Q4_K_M, but Generic Android Phone (8GB RAM) only has ~4.5 GB usable.
Needs ~136.9 GB even at Q4_K_M, but only ~4.5 GB is usable.
- Q4_K_M needed
- ~136.9 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
- 235B (MoE, 22B active)
- Q4_K_M size
- 132.39 GB
- Context
- 128k
- Ollama tag
- qwen3:235b
- Memory
- 8 GB ram
- Usable for weights
- ~4.5 GB
- Best runtime
- llama.cpp (PocketPal or SmolChat)
What you can run instead
Run Qwen3 235B A22B on other hardware
FAQ
Can Generic Android Phone (8GB RAM) run Qwen3 235B A22B?
No. Qwen3 235B A22B needs ~136.9 GB even at Q4_K_M, but Generic Android Phone (8GB RAM) only has ~4.5 GB usable.
How much memory does Qwen3 235B A22B need?
Generic Android Phone (8GB RAM) does not have enough memory. At Q4_K_M the weights are ~132.39 GB; with KV cache and runtime overhead, budget ~136.9 GB at a 4k context. It is a Mixture-of-Experts model (235B total / 22B active), so all experts must stay in memory; memory tracks total params, not active params.
What is the best tool to run Qwen3 235B A22B 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.