Open-weight local LLM

GLM-4 (9B)

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

12 GB catalogue minimum12 GB RAMQ6_KCoding assistant
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Parameters
9B
Minimum RAM
12 GB
Model size
8.3 GB
Quantization
Q6_K

About GLM-4 (9B)

Catalogue summary: Zhipu AI's efficient all-rounder. Strong bilingual performance (CN/EN). Model License (research/personal use; commercial contact Zhipu).

GLM-4 (9B) has a catalogue minimum of 12 GB RAM with Q6_K. Actual memory use and speed vary by context length, runtime, backend and system headroom.

Use glm-4-9b-chat as the catalogue search term in a compatible runtime, and confirm the available format on the upstream repository before download.

chatcodestandardgeneral

Install path

01
Check RAM fitMinimum 12 GB RAM. Start with the Q6_K quant.
02
Load the modelSearch glm-4-9b-chat 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: glm
  • Parameters: 9B
  • Recommended quantization: Q6_K
  • Catalogue minimum RAM: 12 GB
  • Catalogue model size: 8.3 GB
  • Tags: chat, code, standard, 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
8
quality
7
coding
7
reasoning
7

Technical notes

Developer
See upstream repository
License
See upstream repository
Context window
See upstream repository
Architecture
See upstream repository

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