text model · North · macOS
Can I run North Mini Code 1.0 on Apple M1 (8GB)?
No. North Mini Code 1.0 needs ~21.1 GB even at Q4_K_M, but Apple M1 (8GB) only has ~5.5 GB usable.
Needs ~21.1 GB even at Q4_K_M, but only ~5.5 GB is usable.
That figure is at a 4k context and moves about ±15% as context length changes.
The gap is about 15.6 GB: North Mini Code 1.0 needs roughly 21.1 GB at Q4_K_M and Apple M1 (8GB) leaves only about 5.5 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
- ~5.5 GB
- Device memory
- 8 GB
Which quant fits
- 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 unified
- Usable for weights
- ~5.5 GB
- Power draw
- ~39 W
- Best runtime
- Ollama (llama.cpp Metal backend)
What you can run instead
Run North Mini Code 1.0 on other hardware
FAQ
Can Apple M1 (8GB) run North Mini Code 1.0?
No. North Mini Code 1.0 needs ~21.1 GB even at Q4_K_M, but Apple M1 (8GB) only has ~5.5 GB usable.
How much memory does North Mini Code 1.0 need?
Apple M1 (8GB) 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 macOS?
LM Studio for a simple setup; mlx-lm for the most speed. vLLM is NOT a Mac tool, it is a CUDA/Linux serving engine. Unified memory is not a fixed VRAM slice; ~70% is usable for weights.
Embed this
[](https://localmodel.run/can-i-run/north-mini-code-1.0/apple-m1-8gb) 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.