Model reference · open weights

glm-edge-v

LLMs zai-org Vision + text 1 build Its own licence terms 837 dl/mo

glm-edge-v is an open-weight language model from zai-org. glm-edge-v-5b-gguf (GGUF) weighs 2.6 GB; the smallest configuration that runs it is RTX 3060 12 GB.

For self-hosting, compare this build's requirements below with the GPU you plan to rent.

  • GLM-Edge-V is an image-text-to-text model developed by zai-org and published as zai-org/glm-edge-v-5b-gguf.
  • It supports Chinese and English and is distributed under an "other" license.
  • The model is designed for inference via the llama.cpp framework, with specific code adaptations available for integration.

Summary of the zai-org/glm-edge-v-5b-gguf model card, 2026-10-01

What it is

Released byzai-org
Released2024-11-27
VRAM2.6 GB for the weights

What it runs on

Memory and cards for glm-edge-v-5b-gguf (GGUF)

2.6 GBweights, file size
651 MBruntime overhead, at least

How much memory each request adds isn't estimated yet for this architecture. The weights need at least the cards below, plus room for the context.

CardWeights alone
RTX 3060 12 GBfits
RTX 4060 Ti 16 GBfits
RTX 3090 24 GBfits
RTX 4090 24 GBfits
RTX 5090 32 GBfits
L40S 48 GBfits
A100 80 GBfits
H100 80 GBfits
RTX PRO 6000 Blackwell 96 GBfits
DGX Spark (GB10) 128 GB unifiedfits
H200 141 GBfits
B200 180 GBfits

From the model card

What zai-org says about glm-edge-v

Read the model card

中文阅读, 点击这里

Inference with Ollama

Installation

The code for adapting this model is actively being integrated into the official llama.cpp. You can test it using the following adapted version:

git clone https://github.com/piDack/llama.cpp -b support_glm_edge_model
cmake -B build -DGGML_CUDA=ON # Or enable other acceleration hardware
cmake --build build -- -j

Inference

After installation, you can start the GLM-Edge Chat model using the following command:

llama-cli -m /model.gguf -p "\nhi\n" -ngl 999

In the command-line interface, you can interact with the model by entering your requests, and the model will provide the corresponding responses.

License

The usage of this model’s weights is subject to the terms outlined in the LICENSE.

Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.

How it works

How language models work

Your prompttext / messagesTransformerattention over tokensNext-token loopgenerate + streamResponsetext · tool callsA language model reads your tokens and predicts the next one, again and again, streaming the reply back.
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