text model · Qwen3 · Android
Can I run Qwen3 32B on Google Pixel 10 Pro?
No. Qwen3 32B needs ~22 GB even at Q4_K_M, but Google Pixel 10 Pro only has ~10.5 GB usable.
Needs ~22 GB even at Q4_K_M, but only ~10.5 GB is usable.
That figure is at a 4k context and moves about ±15% as context length changes.
The gap is about 11.5 GB: Qwen3 32B needs roughly 22 GB at Q4_K_M and Google Pixel 10 Pro leaves only about 10.5 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
- ~10.5 GB
- Device memory
- 16 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
- 16 GB ram
- Usable for weights
- ~10.5 GB
- Power draw
- ~7 W
- Best runtime
- llama.cpp (PocketPal) or MLC-LLM (Adreno GPU path)
What you can run instead
Run Qwen3 32B on other hardware
FAQ
Can Google Pixel 10 Pro run Qwen3 32B?
No. Qwen3 32B needs ~22 GB even at Q4_K_M, but Google Pixel 10 Pro only has ~10.5 GB usable.
How much memory does Qwen3 32B need?
Google Pixel 10 Pro 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/pixel-10-pro) Sources
- 9to5google.com
- aider.chat
- gadgetversus.com
- github.com/a-ghorbani
- github.com/google-ai-edge
- github.com/mlc-ai
- github.com/shubham0204
- github.com/Vali-98
- gorilla.cs.berkeley.edu
- gsmarena.com
- huggingface.co
- layla-network.ai
- lmarena.ai
- ollama.com
- qwenlm.github.io
- store.google.com/product/pixel_10_pro
- store.google.com/product/pixel_10_pro_specs
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.