text model · Qwen3 · Android
Can I run Qwen3 8B on Samsung Galaxy S26 Ultra (16GB, 1TB config)?
Yes. Qwen3 8B runs on Samsung Galaxy S26 Ultra (16GB, 1TB config) at Q4_K_M (~6.5 GB of ~12 GB usable).
Runs at Q4_K_M using ~6.5 GB of ~12 GB usable. You have room for Q8_0 for higher quality.
That figure is at a 4k context and moves about ±15% as context length changes. Samsung Galaxy S26 Ultra (16GB, 1TB config) leaves ~5.5 GB of headroom, room to step up to Q8_0 for higher quality.
- Q4_K_M needed
- ~6.5 GB
- Usable on device
- ~12 GB
- Device memory
- 16 GB
- Best quant
- Q4_K_M
Which quant fits
How to run it
On Android use PocketPal AI (Polished app, download GGUF and run offline.).
- Parameters
- 8B
- Q4_K_M size
- 5.03 GB
- Q8_0 size
- 8.71 GB
- Context
- 32k
- Ollama tag
- qwen3:8b
- Memory
- 16 GB ram
- Usable for weights
- ~12 GB
- Best runtime
- llama.cpp (PocketPal) or MLC-LLM (Adreno GPU path)
You could also run
Run Qwen3 8B on other hardware
FAQ
Can Samsung Galaxy S26 Ultra (16GB, 1TB config) run Qwen3 8B?
Yes. Qwen3 8B runs on Samsung Galaxy S26 Ultra (16GB, 1TB config) at Q4_K_M (~6.5 GB of ~12 GB usable).
How much memory does Qwen3 8B need?
Samsung Galaxy S26 Ultra (16GB, 1TB config) has room to spare. At Q4_K_M the weights are ~5.03 GB; with KV cache and runtime overhead, budget ~6.5 GB at a 4k context.
What is the best tool to run Qwen3 8B 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.
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A live badge for your model card or README, updated as the data is. [](https://localmodel.run/can-i-run/qwen3-8b/samsung-s26-ultra) 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.