text model · North · Android
Can I run North Mini Code 1.0 on Samsung Galaxy S25 Ultra (16GB, 1TB config only)?
No. North Mini Code 1.0 needs ~21.1 GB even at Q4_K_M, but Samsung Galaxy S25 Ultra (16GB, 1TB config only) only has ~12 GB usable.
Needs ~21.1 GB even at Q4_K_M, but only ~12 GB is usable.
That figure is at a 4k context and moves about ±15% as context length changes.
The gap is about 9.1 GB: North Mini Code 1.0 needs roughly 21.1 GB at Q4_K_M and Samsung Galaxy S25 Ultra (16GB, 1TB config only) leaves only about 12 GB usable for a model. The lightest tracked hardware that runs North Mini Code 1.0 is the Nvidia GeForce RTX 4090 (24GB) at 24 GB. See North Mini Code 1.0 on Nvidia GeForce RTX 4090 (24GB).
- Q4_K_M needed
- ~21.1 GB
- Usable on device
- ~12 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, 3B active)
- Q4_K_M size
- 19 GB
- Q8_0 size
- 32 GB
- Context
- 500k
- Ollama tag
- north-mini-code-1.0
- Memory
- 16 GB ram
- Usable for weights
- ~12 GB
- Power draw
- ~8 W
- Best runtime
- llama.cpp (PocketPal) or MLC-LLM (Adreno GPU path)
What you can run instead
Run North Mini Code 1.0 on other hardware
FAQ
Can Samsung Galaxy S25 Ultra (16GB, 1TB config only) run North Mini Code 1.0?
No. North Mini Code 1.0 needs ~21.1 GB even at Q4_K_M, but Samsung Galaxy S25 Ultra (16GB, 1TB config only) only has ~12 GB usable.
How much memory does North Mini Code 1.0 need?
Samsung Galaxy S25 Ultra (16GB, 1TB config only) does not have enough memory. At Q4_K_M the weights are ~19 GB; with KV cache and runtime overhead, budget ~21.1 GB at a 4k context. It is a Mixture-of-Experts model (30.5B total / 3B active), so all experts must stay in memory; memory tracks total params, not active params.
What is the best tool to run North Mini Code 1.0 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
[](https://localmodel.run/can-i-run/north-mini-code-1.0/samsung-s25-ultra-16gb) Sources
- gadgetversus.com
- github.com/a-ghorbani
- github.com/google-ai-edge
- github.com/mlc-ai
- github.com/shubham0204
- github.com/Vali-98
- gsmarena.com/samsung_galaxy_s25_ultra-12750.php
- gsmarena.com/samsung_galaxy_s25_ultra-13322.php
- huggingface.co/bartowski
- huggingface.co/CohereLabs
- layla-network.ai
- ollama.com
- sammyfans.com
- sammyguru.com
- samsung.com
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.