text model · Qwen3.6 · Android
Can I run Qwen3.6 35B-A3B on Generic Android Phone (8GB RAM)?
No. Qwen3.6 35B-A3B needs ~24.5 GB even at Q4_K_M, but Generic Android Phone (8GB RAM) only has ~4.5 GB usable.
Needs ~24.5 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 20 GB: Qwen3.6 35B-A3B needs roughly 24.5 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 Qwen3.6 35B-A3B is the Nvidia GeForce RTX 5090 (32GB) at 32 GB. See Qwen3.6 35B-A3B on Nvidia GeForce RTX 5090 (32GB).
- Q4_K_M needed
- ~24.5 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
- 36B (MoE, 3B active)
- Q4_K_M size
- 22.29 GB
- Q8_0 size
- 36.9 GB
- Context
- 256k
- Ollama tag
- qwen3.6:35b-a3b
- Memory
- 8 GB ram
- Usable for weights
- ~4.5 GB
- Best runtime
- llama.cpp (PocketPal or SmolChat)
What you can run instead
Run Qwen3.6 35B-A3B on other hardware
FAQ
Can Generic Android Phone (8GB RAM) run Qwen3.6 35B-A3B?
No. Qwen3.6 35B-A3B needs ~24.5 GB even at Q4_K_M, but Generic Android Phone (8GB RAM) only has ~4.5 GB usable.
How much memory does Qwen3.6 35B-A3B need?
Generic Android Phone (8GB RAM) does not have enough memory. At Q4_K_M the weights are ~22.29 GB; with KV cache and runtime overhead, budget ~24.5 GB at a 4k context. It is a Mixture-of-Experts model (36B total / 3B active), so all experts must stay in memory; memory tracks total params, not active params.
What is the best tool to run Qwen3.6 35B-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/qwen3.6-35b-a3b/android-generic-8gb) Sources
- github.com/a-ghorbani
- github.com/google-ai-edge
- github.com/mlc-ai
- github.com/shubham0204
- github.com/Vali-98
- huggingface.co/bartowski/Llama-3.2-1B-Instruct-GGUF
- huggingface.co/bartowski/Qwen_Qwen3.6-35B-A3B-GGUF
- huggingface.co/Qwen
- huggingface.co/unsloth
- layla-network.ai
- ollama.com/library/llama3.2:1b
- ollama.com/library/qwen3.6
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.