text model · Qwen3 · Android
Can I run Qwen3 30B-A3B on Google Pixel 10 Pro?
No. Qwen3 30B-A3B needs ~20.7 GB even at Q4_K_M, but Google Pixel 10 Pro only has ~10.5 GB usable.
Needs ~20.7 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 10.2 GB: Qwen3 30B-A3B needs roughly 20.7 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 30B-A3B is the Nvidia GeForce RTX 4090 (24GB) at 24 GB. See Qwen3 30B-A3B on Nvidia GeForce RTX 4090 (24GB).
- Q4_K_M needed
- ~20.7 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
- 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
- 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 30B-A3B on other hardware
FAQ
Can Google Pixel 10 Pro run Qwen3 30B-A3B?
No. Qwen3 30B-A3B needs ~20.7 GB even at Q4_K_M, but Google Pixel 10 Pro only has ~10.5 GB usable.
How much memory does Qwen3 30B-A3B need?
Google Pixel 10 Pro 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.
Embed this
A live badge for your model card or README, updated as the data is. [](https://localmodel.run/can-i-run/qwen3-30b-a3b/pixel-10-pro) Sources
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9to5google.com · 1 source
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gadgetversus.com · 1 source
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github.com · 5 sources
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gsmarena.com · 1 source
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huggingface.co · 2 sources
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layla-network.ai · 1 source
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lmarena.ai · 1 source
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ollama.com · 1 source
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qwenlm.github.io · 1 source
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store.google.com · 2 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.