text model · SmolLM2 · Android
Can I run SmolLM2 135M on Samsung Galaxy S26 Ultra (16GB, 1TB config)?
Yes. SmolLM2 135M runs on Samsung Galaxy S26 Ultra (16GB, 1TB config) at Q4_K_M (~1 GB of ~12 GB usable).
Runs at Q4_K_M using ~1 GB of ~12 GB usable. You have room for FP16 for higher quality.
- Q4_K_M needed
- ~1 GB
- Usable on device
- ~12 GB
- Device memory
- 16 GB
- Best quant
- Q4_K_M
How to run it
On Android use PocketPal AI (Polished app, download GGUF and run offline.). NPU acceleration is limited and chip-specific; most apps run on CPU. Expect 1B-4B class.
- Parameters
- 0.135B
- Q4_K_M size
- 0.105 GB
- Q8_0 size
- 0.145 GB
- Context
- 2k
- Ollama tag
- smollm2:135m
- 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 SmolLM2 135M on other hardware
FAQ
Can Samsung Galaxy S26 Ultra (16GB, 1TB config) run SmolLM2 135M?
Yes. SmolLM2 135M runs on Samsung Galaxy S26 Ultra (16GB, 1TB config) at Q4_K_M (~1 GB of ~12 GB usable).
How much memory does SmolLM2 135M need?
Samsung Galaxy S26 Ultra (16GB, 1TB config) has room to spare. At Q4_K_M the weights are ~0.105 GB; with KV cache and runtime overhead, budget ~1 GB at a 4k context.
What is the best tool to run SmolLM2 135M 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.
Sources
Memory figures are estimates. See methodology.