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LatestZhipu AI4.6
Zhipu AIText / LLM

Zhipu AI Releases Open-Weight MoE Model GLM-4.6

The new Mixture-of-Experts model is available under a permissive MIT license and is optimized for complex reasoning and coding tasks.

Sep 29, 2025
Major releaseMIT
GLM-4.6

Zhipu AI, one of China's leading AI labs, has released GLM-4.6, a new large language model with open weights. The model utilizes a Mixture-of-Experts (MoE) architecture, a design known for balancing high performance with more efficient inference compared to dense models of similar capability.

GLM-4.6 is positioned as a strong performer in complex tasks that require sophisticated logical deduction. According to the development team, the model shows significant improvements in both logical reasoning and code generation abilities, making it a powerful tool for developers and researchers.

Why it matters

Perhaps most notable is the model's release under the MIT license. This permissive license allows for broad use, including commercial applications, removing a common barrier for businesses looking to build on top of state-of-the-art open models. The release of a capable MoE model from a major international lab under such terms is a significant contribution to the open-source ecosystem.

Researchers and developers can access the model weights and further details directly from the official repository on Hugging Face. The release provides another powerful, commercially-friendly foundation model for the global AI community.

Sources

  • zai-org/GLM-4.6

    Hugging Face

    Visit
OlderMing-UniAudio Brings MoE to Unified Audio AIinclusionAI · Any-to-Any · last yearNewerOvi Syncs Audio and Video in New Open-Source Modelchetwinlow1 · Image → Video · last year

Get the model

Hugging Face

Specs

Context window203K tokens
Size713.6 GB
PrecisionBF16
ArchitectureGlm4MoeForCausalLM
LicenseMIT
Downloads25.4K
Likes1.2K

Can you run it?

Needs a multi-GPU server — about 218.3 GB at 4-bit.

  • BF16 (as published)

    Multi-node cluster

    717.7 GB
  • 8-bit

    8×H100 80 GB node

    382.5 GB
  • 4-bit

    4×H100 80 GB · 512 GB Mac Studio

    218.3 GB
Your machine
GGUF builds
  • Mixture-of-experts: every expert must be loaded, so memory follows total parameters, not the active slice.
  • KV cache sized for 8,192 tokens of context; longer prompts need proportionally more.

Based on Hugging Face weights metadata. Assumes an 8K context and ~1 GB runtime overhead; actual needs vary. How we estimate


Modalities

Text / LLMReasoning
2 versions — view changelog

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