North Mini Code — Cohere 的本地 AI 模型

作者: Jakub Rusinowski · 最后更新:

Cohere's first model aimed at developers rather than enterprises, and the opening entry in their North family of code agents. A 30B sparse MoE activating ~3B per token, Apache 2.0, 256K context and 64K maximum generation — small enough to run locally while posting a Coding Index of 33.4, ahead of several open models many times its size.

变体

North Mini Code 最小的变体在 Q4_K_M 下约需 19 GB 显存——量化权重加框架开销,不含 KV 缓存。

模型显存
North Mini Code 1.0 30B-A3B →
30B (3B active)
~18.9 GB

显存为 Q4_K_M 下的量化权重加开销,与 GPU 与显存检测器使用同一引擎计算。

如何在本地运行 North Mini Code

安装 Ollama,然后拉取标签。

ollama run north-mini-code

在上方选择一个尺寸,查看它自己的显存、速度估算和安装命令。

许可证

Apache-2.0允许商业使用

Commercial use permitted. No usage restrictions beyond attribution.

适用于: North Mini Code 1.0 30B-A3B

推荐 GPU

目录中能在本地运行 North Mini Code(至少 19 GB 显存)的最便宜 GPU 是 AMD Radeon RX 7900 XTX (24 GB).

联盟营销声明: 本页部分链接为联盟推广链接——如果你通过它们购买,LLM Configurator 可能会获得佣金,而你无需支付任何额外费用。作为亚马逊联盟成员(Amazon Associate),LLM Configurator 会从符合条件的购买中获得收益。
AMD Radeon RX 7900 XTX 24GB
24 GB VRAM · 355 W board power
2026年价格波动较大——请以当前商品页价格为准。

我的 GPU 能运行 North Mini Code 吗?

North Mini Code — 常见问题

How much VRAM does North Mini Code need?

North Mini Code needs about 19 GB VRAM at Q4_K_M quantization for its smallest variant. Variants: North Mini Code 1.0 30B-A3B (19 GB, Q4_K_M). On Apple Silicon, unified memory counts toward this requirement.

Can I run North Mini Code on an RTX 4090 (24 GB)?

Yes — North Mini Code runs on an RTX 4090 (24 GB) and other 24 GB cards such as the RTX 3090. Smaller variants also fit comfortably on 8–16 GB GPUs at Q4_K_M.

What quantization should I use for North Mini Code?

Q4_K_M is the best balance of quality and VRAM for North Mini Code in most cases. Choose Q8_0 for near-lossless quality if you have spare VRAM, or smaller quants (Q3/Q2) only when memory is tight.

How do I run North Mini Code with Ollama?

North Mini Code has no local Ollama tag — the published tag is cloud-hosted, so running it sends your prompts to a hosted GPU rather than your own machine.