text model · North · Android
Can I run North Mini Code 1.0 on Generic Android Phone (8GB RAM)?
No. North Mini Code 1.0 needs ~21.1 GB even at Q4_K_M, but Generic Android Phone (8GB RAM) only has ~4.5 GB usable.
Needs ~21.1 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 16.6 GB: North Mini Code 1.0 needs roughly 21.1 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 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
- ~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
- 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
- 8 GB ram
- Usable for weights
- ~4.5 GB
- Best runtime
- llama.cpp (PocketPal or SmolChat)
What you can run instead
Run North Mini Code 1.0 on other hardware
FAQ
Can Generic Android Phone (8GB RAM) run North Mini Code 1.0?
No. North Mini Code 1.0 needs ~21.1 GB even at Q4_K_M, but Generic Android Phone (8GB RAM) only has ~4.5 GB usable.
How much memory does North Mini Code 1.0 need?
Generic Android Phone (8GB RAM) 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
A live badge for your model card or README, updated as the data is. [](https://localmodel.run/can-i-run/north-mini-code-1.0/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/North-Mini-Code-1.0-GGUF
- huggingface.co/CohereLabs
- layla-network.ai
- ollama.com/library/llama3.2:1b
- ollama.com/library/north-mini-code-1.0
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.