Open-weight MoE

GLM 4.7

Catalogue summary: Z.ai open-weight flagship MoE with 355B total parameters and 32B active per token. The verified Q4_K_M GGUF is roughly 216.5 GB, so local use requires a 256 GB workstation class. MIT licensed.

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

256 GB catalogue minimum256 GB RAMQ4_K_MCoding assistant
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This is an advanced workstation/server GGUF path. Unsupported apps are clearly marked.

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Parameters
355B (32B active, MoE)
Minimum RAM
256 GB
Model size
216.5 GB
Quantization
Q4_K_M

Can GLM 4.7 run locally?

GLM 4.7 has a catalogue minimum of 256 GB RAM with Q4_K_M. Actual memory use and speed vary by context length, runtime, backend and system headroom.

Treat glm-4.7 as an advanced workstation/server-grade catalogue target with verified GGUF artefacts. Open the GGUF files and follow the upstream runtime notes; LocalClaw does not claim a one-click LM Studio install.

chatcodepowerqualitygeneral

Deployment path

01
Check RAM fitServer-grade target. Plan for 256 GB class multi-GPU memory.
02
Load the modelOpen the verified GGUF files for glm-4.7 and follow the upstream runtime or patch notes first.
03
Control locallyUse LocalClaw to manage models, agents, chat, channels and scheduled OpenClaw work.

Catalogue record

  • Family: glm
  • Parameters: 355B (32B active, MoE)
  • Recommended quantization: Q4_K_M
  • Catalogue minimum RAM: 256 GB
  • Catalogue model size: 216.5 GB
  • Tags: chat, code, power, quality, 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.

Catalogue tags

  • chat
  • code
  • power
  • quality
  • general

Capability profile

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

speed
5
quality
9
coding
9
reasoning
9

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