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LatestZhipu AI4.5V
Zhipu AIVision-Language

Zhipu AI Releases Open Vision Model GLM-4.5V

The new Mixture-of-Experts model offers strong multimodal reasoning capabilities under a permissive MIT license.

Aug 10, 2025
Major releaseMIT
GLM-4.5V

Chinese AI firm Zhipu AI has released GLM-4.5V, a new open-source vision-language model (VLM). The model, which uses a Mixture-of-Experts (MoE) architecture, is designed for sophisticated tasks that require understanding and reasoning about both text and images simultaneously.

According to the release notes, GLM-4.5V is built upon the company's GLM-4.5-Air-Base model. The key advancement is its capacity for what Zhipu AI describes as strong multimodal reasoning. This makes it suitable for complex applications like detailed image analysis, visual question answering, and generating text grounded in visual information. The model weights and code are available now on Hugging Face.

Why it matters

The release is significant for two main reasons. First, it adds a powerful, openly accessible VLM to the ecosystem, a domain where proprietary models have often dominated. Second, its release under the permissive MIT license removes significant barriers for both commercial and research applications, allowing developers to freely build upon and integrate the technology.

The MoE architecture also suggests an efficient design, capable of activating only the necessary expert sub-networks during inference. This can lead to faster performance and lower computational costs compared to dense models of a similar capability level, making advanced multimodal AI more accessible to a wider range of developers and organizations.

Sources

  • zai-org/GLM-4.5V

    Hugging Face

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Get the model

Hugging Face

Specs

Active params12B active
Context window66K tokens
Size215.4 GB
PrecisionBF16
ArchitectureGlm4vMoeForConditionalGeneration
LicenseMIT
Downloads19.9K
Likes726

Can you run it?

Needs a workstation GPU (96 GB) at 4-bit.

  • BF16 (as published)

    4×H100 80 GB · 512 GB Mac Studio

    217.9 GB
  • 8-bit

    H200 141 GB or 2×80 GB · 192 GB Mac Studio

    117 GB
  • 4-bit

    80 GB GPU (A100 / H100) · 128 GB Mac Studio

    67.3 GB
Your machine
GGUF builds
  • Mixture-of-experts: all 108B parameters must be resident even though only 12B are active per token — active count affects speed, not memory.
  • 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

Vision-LanguageReasoning

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