text model · North · macOS
Can I run North Mini Code 1.0 on Apple M5 Pro (48GB)?
Yes. North Mini Code 1.0 runs on Apple M5 Pro (48GB) at Q4_K_M (~21.1 GB of ~32 GB usable).
Runs at Q4_K_M using ~21.1 GB of ~32 GB usable.
That figure is at a 4k context and moves about ±15% as context length changes. Apple M5 Pro (48GB) leaves ~10.9 GB of headroom.
- Q4_K_M needed
- ~21.1 GB
- Usable on device
- ~32 GB
- Device memory
- 48 GB
- Best quant
- Q4_K_M
Which quant fits
Run it
Pick your tool. All 3 load the same Q4_K_M weights.
ollama run north-mini-code-1.0 llama-cli -hf bartowski/North-Mini-Code-1.0-GGUF:Q4_K_M lms get bartowski/North-Mini-Code-1.0-GGUF - 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
- 48 GB unified
- Usable for weights
- ~32 GB
- Best runtime
- MLX direct / Ollama (MLX backend)
You could also run
Run North Mini Code 1.0 on other hardware
FAQ
Can Apple M5 Pro (48GB) run North Mini Code 1.0?
Yes. North Mini Code 1.0 runs on Apple M5 Pro (48GB) at Q4_K_M (~21.1 GB of ~32 GB usable).
How much memory does North Mini Code 1.0 need?
Apple M5 Pro (48GB) has room to spare. 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.
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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/apple-m5-pro-48gb) 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.