Open-weight local LLM

MiniCPM5 2B

Use case: Coding assistant. Check the memory fit and choose a setup path below.

4 GB catalogue minimum4 GB RAMQ4_K_MCoding assistant
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Parameters
2B
Minimum RAM
4 GB
Model size
1.6 GB
Quantization
Q4_K_M

About MiniCPM5 2B

Catalogue summary: Official OpenBMB compact on-device LLM with Apache 2.0 licensing, 131K context, tool-calling and coding focus, plus official Q4_K_M GGUF, Ollama, llama.cpp, Docker and OpenClaw run paths.

MiniCPM5 2B has a catalogue minimum of 4 GB RAM with Q4_K_M. Actual memory use and speed vary by context length, runtime, backend and system headroom.

Use minicpm5-2b as the catalogue search term in a compatible runtime, and confirm the available format on the upstream repository before download.

chatcodereasoningagentlightlong-contexttool-callinggeneral

Install path

01
Check RAM fitMinimum 4 GB RAM. Start with the Q4_K_M quant.
02
Load the modelSearch minicpm5-2b in LM Studio.
03
Try your workloadStart with a short prompt, check memory use and adjust context length before running longer tasks.

Catalogue record

  • Family: minicpm
  • Parameters: 2B
  • Recommended quantization: Q4_K_M
  • Catalogue minimum RAM: 4 GB
  • Catalogue model size: 1.6 GB
  • Tags: chat, code, reasoning, agent, light, long-context, tool-calling, general

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.

Reading the scores

LocalClaw /10 is an editorial catalogue rating. Community /5 comes from member reviews. Your machine fit is a separate memory and runtime assessment.

Capability profile

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

speed
10
quality
7
coding
7
reasoning
7

Technical notes

Developer
OpenBMB (Tsinghua)
License
Apache 2.0
Context window
131,072 tokens
Architecture
Dense MiniCPM5 decoder-only language model with 2.5B total parameters, 42 layers, grouped-query attention and long-context/tool-calling oriented post-training.

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.

Related catalogue entries

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

Where to go next