LocalClaw/LLM/North Mini Code 1.0
Open-weight MoE

North Mini Code 1.0

Catalogue summary: Cohere Labs Apache 2.0 coding and agent model. 30B total / 3B active MoE, 256K context, terminal-task training and mature GGUF quantizations for local workstation use.

Repository editorial metadata; verify comparative claims in the linked upstream material.

32 GB catalogue minimum32 GB RAMQ4_K_MCoding assistant
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Parameters
30B (3B active, MoE)
Minimum RAM
32 GB
Model size
18 GB
Quantization
Q4_K_M

Can North Mini Code 1.0 run locally?

North Mini Code 1.0 has a catalogue minimum of 32 GB RAM with Q4_K_M. Actual memory use and speed vary by context length, runtime, backend and system headroom.

Use north-mini-code-1.0 as the catalogue search term in a compatible runtime, and confirm the available format on the upstream repository before download.

Model sourcePublic GGUF repository (verified 2026-09-07): unsloth/North-Mini-Code-1.0-GGUFUpstream releasePaper or model cardLicence text
codeagentreasoningpower

Install path

01
Check RAM fitMinimum 32 GB RAM. Start with the Q4_K_M quant.
02
Load the modelSearch north-mini-code-1.0 in LM Studio.
03
Control locallyUse LocalClaw to manage models, agents, chat, channels and scheduled OpenClaw work.

Catalogue record

  • Family: cohere
  • Parameters: 30B (3B active, MoE)
  • Recommended quantization: Q4_K_M
  • Catalogue minimum RAM: 32 GB
  • Catalogue model size: 18 GB
  • Tags: code, agent, reasoning, power

Practical limits

  • Catalogue RAM is a minimum estimate, not a guarantee for every context length or runtime.
  • Speed and memory use vary by quantization, backend, context length and system headroom.
  • Verify architecture, licence and usage restrictions in the linked upstream material before deployment.

Catalogue tags

  • code
  • agent
  • reasoning
  • power

Capability profile

Repository catalogue ratings used by LocalClaw's editorial rubric. They are not a standardized third-party benchmark.

speed
5
quality
8
coding
9
reasoning
8

Technical notes

Developer
Cohere Labs
License
Apache 2.0
Context window
262,144 tokens
Architecture
Decoder-only sparse Mixture-of-Experts model with 30B total parameters, about 3B active parameters, 128 experts and 8 activated experts per token.

This model fits these next steps

Hardware fit is based on LocalClaw's RAM tier, model size and quantization metadata. Always leave memory headroom for your OS and runtime.

Comfortable headroomMac mini M4 Pro 48GBMobile workstationMacBook Pro M4 Max 36GBPower-user picks32GB RAM guide

Related catalogue entries

Linked mechanically by family, shared tags and nearby RAM tier; this is not a quality ranking.

Command R+ (104B) 104BAya Expanse (32B) 32BCommand R (35B) 35BCommand A (111B) 111BAgents-A1 35B (3B active, MoE)Qwen AgentWorld 35B-A3B 35B (3B active, MoE)Laguna XS 2.1 33B (3B active, MoE)LongCat-Flash-Lite 68.5B (3-4.5B active, MoE)

Where to go next