text model · Qwen3 · Android
Can I run Qwen3 32B on Generic Android Phone (8GB RAM)?
No. Qwen3 32B needs ~22 GB even at Q4_K_M, but Generic Android Phone (8GB RAM) only has ~4.5 GB usable.
Needs ~22 GB even at Q4_K_M, but only ~4.5 GB is usable.
That figure is at a 4k context and moves about ±15% as context length changes.
The gap is about 17.5 GB: Qwen3 32B needs roughly 22 GB at Q4_K_M and Generic Android Phone (8GB RAM) leaves only about 4.5 GB usable for a model. The lightest tracked hardware that runs Qwen3 32B is the Nvidia GeForce RTX 4090 (24GB) at 24 GB. See Qwen3 32B on Nvidia GeForce RTX 4090 (24GB).
- Q4_K_M needed
- ~22 GB
- Usable on device
- ~4.5 GB
- Device memory
- 8 GB
Which quant fits
How to run it
On Android use PocketPal AI (Polished app, download GGUF and run offline.).
- Parameters
- 32B
- Q4_K_M size
- 19.8 GB
- Q8_0 size
- 34.8 GB
- Context
- 32k
- Ollama tag
- qwen3:32b
- Memory
- 8 GB ram
- Usable for weights
- ~4.5 GB
- Best runtime
- llama.cpp (PocketPal or SmolChat)
What you can run instead
Run Qwen3 32B on other hardware
FAQ
Can Generic Android Phone (8GB RAM) run Qwen3 32B?
No. Qwen3 32B needs ~22 GB even at Q4_K_M, but Generic Android Phone (8GB RAM) only has ~4.5 GB usable.
How much memory does Qwen3 32B need?
Generic Android Phone (8GB RAM) does not have enough memory. At Q4_K_M the weights are ~19.8 GB; with KV cache and runtime overhead, budget ~22 GB at a 4k context.
What is the best tool to run Qwen3 32B 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.
Embed this
A live badge for your model card or README, updated as the data is. [](https://localmodel.run/can-i-run/qwen3-32b/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.