Image generation

GLM-Image

A hybrid of a 9B language model and a 7B decoder. Strong at text-heavy images: posters, infographics, slides.

Developer
Zhipu AI (Z.ai), China
First release
Jan 2026
Latest release
Jan 2026
Sizes
9B + 7B
License
Commercial use allowedMIT
Running
On your own serverNeeds a GPU
Industries
Marketing and content, Education
Deploy on your server Hugging Face GitHub

What it does

  • Posters and banners with text
  • Infographics
  • Illustrations for presentations

Where it is used

MarketingEducationInternal communications

Hardware requirements

LaptopLaptop or regular PC, up to 8 GB of VRAM — smaller versions
no versions
1 GPUOne GPU with 16–80 GB — mid-size versions
fits
ClusterServer with several GPUs — flagship versions
no versions

Versions

  1. GLM-Image

How to run it

On your own serverWeights are downloaded from Hugging Face and served with vLLM (load and API) or llama.cpp (modest hardware). The model then runs in a closed network with no per-request fees.Hugging Face
How much hardware you needCalculate the VRAM for the model size, context length and number of concurrent requests.Open the hardware calculator

I can set this up end to end: pick the model size, deploy it on your server and connect it to your systems.

Frequently asked questions

Can GLM-Image be used in a commercial project?

Yes. License: MIT. It allows commercial use, but it is still worth having a lawyer review the license before launch.

What hardware does GLM-Image need?

At minimum: One GPU with 16–80 GB — mid-size versions. Without a GPU the model is not practical. You can calculate the exact VRAM for your model size and context in the hardware calculator.

Does GLM-Image support Russian?

Language does not matter for this model: it does not work with text.

Where can I download GLM-Image and what does it cost?

The GLM-Image weights are open and free to download. You only pay for the hardware it runs on and for the setup. Source links are at the bottom of this page.

How I deploy it for clients

  1. SelectionI pick the model size for your task and hardware and test it on your examples.
  2. DeploymentI deploy it on your server or in a closed network and provide an API.
  3. Fine-tuningI fine-tune it on your data (LoRA) or connect a knowledge base — whichever is cheaper for the task.
  4. IntegrationI connect it to your CRM, ERP, bot, website or team chat and set up monitoring.

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Details

Source: huggingface.co/zai-org/GLM-Image. Data checked against the model card on 22 Sep 2026. Have a lawyer review the license before commercial launch.