What are the hardware requirements for GLM OCR?

GLM OCR requires approximately 2.6 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed GLM OCR and how large is it?

GLM OCR is developed by zai-org, a model with 1.3b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

Where can I download or evaluate GLM OCR?

GLM OCR is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

GLM OCR by zai-org — VRAM 2.6GB

Name: GLM OCR
Author: zai-org

GLM OCR is an open-source language model by zai-org. Features: 1.3b LLM, VRAM: 2.6GB, License: mit, LLM Explorer Score: 0.44.

Arxiv:2603.10910 Conversational De Deploy:azure Deploy:sagemaker En Endpoints compatible Es Eval-results Fr Glm ocr Image-text-to-text Ja Ko Region:us Ru Safetensors Zh

Model Card on HF 🤗: https://huggingface.co/zai-org/GLM-OCR

GLM OCR Parameters and Internals

LLM Name	GLM OCR
Repository 🤗	https://huggingface.co/zai-org/GLM-OCR
Model Size	1.3b
Required VRAM	2.6 GB
Updated	2026-08-08
Maintainer	zai-org
Model Type	glm_ocr
Model Files	2.6 GB
Supported Languages	zh en fr es ru de ja ko
Model Architecture	GlmOcrForConditionalGeneration
License	mit
Model Max Length	655380
Transformers Version	5.0.1dev0
Tokenizer Class	TokenizersBackend
Padding Token	<\|endoftext\|>

Rank the GLM OCR capabilities

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Instruction Following and Task Automation
Factuality and Completeness of Knowledge
Censorship and Alignment
Data Analysis and Insight Generation
Text Generation
Text Summarization and Feature Extraction
Code Generation
Multi-Language Support and Translation

Best Alternatives to GLM OCR

Best Alternatives	Context / RAM	Downloads	Likes
GLM OCR	0K / 2.6 GB	31416	36