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LatestZhipu AIGLM-4.5
Zhipu AIText / LLM

Z.ai Releases 355B Parameter GLM-4.5 Under MIT License

The new Mixture-of-Experts model combines massive scale with a fully permissive license, targeting complex reasoning and agentic applications.

Jul 20, 2025
Major releaseMIT
GLM-4.6

Z.ai has introduced GLM-4.5, a powerful new open-source model designed for complex reasoning and agentic tasks. The model utilizes a Mixture-of-Experts (MoE) architecture with a total of 355 billion parameters, placing it firmly in the top tier of openly available large language models.

The most significant aspect of this release may be its license. GLM-4.5 is available under the permissive MIT license, a notable decision that removes the commercial use restrictions often seen with other flagship-class model releases. This choice allows developers and businesses to freely build and deploy applications on a state-of-the-art foundation.

Beyond Text Generation

Beyond its scale, GLM-4.5 is explicitly engineered for more than simple text completion. The model's architecture is optimized for hybrid reasoning, enabling it to function as the core of an autonomous agent that can understand user intent and execute multi-step tasks. Key capabilities include:

  • Interacting with external tools and APIs.
  • Planning and executing complex workflows.
  • Performing sophisticated data analysis.

With a context length of 131,072 tokens, the model can process and reason over extensive documents. Developers can access the model weights and documentation on the official Hugging Face repository.

Sources

  • zai-org/GLM-4.5

    Hugging Face

    Visit
OlderQwen Releases Wan 2.2, a 5B Open Video AI ModelQwen · Alibaba · Text → Video · last yearNewerQwen Releases 480B Open-Source Model for Code AgentsQwen · Alibaba · Code · last year

Get the model

Hugging Face

Specs

Parameters355B · MoE
Context window131K tokens
Size716.7 GB
PrecisionBF16
ArchitectureGlm4MoeForCausalLM
LicenseMIT
Downloads25.4K
Likes1.2K

Can you run it?

Needs a multi-GPU server — about 218.9 GB at 4-bit.

  • BF16 (as published)

    Multi-node cluster

    720.8 GB
  • 8-bit

    8×H100 80 GB node

    383.6 GB
  • 4-bit

    4×H100 80 GB · 512 GB Mac Studio

    218.9 GB
Your machine
  • 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

ReasoningText / LLMCode
2 versions — view changelog

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