GLM-4 is an open-source AI model developed by Zhipu AI (THUDM). It can be deployed on-premises with the right GPU hardware for private, secure AI inference.

How much VRAM does GLM-4 require?

VRAM requirements for GLM-4 depend on the quantization level. Full-precision models need more VRAM, while quantized versions (Q4, Q5, Q8) can run on consumer GPUs. See our VRAM requirements table for specific recommendations.

Can I run GLM-4 locally?

Yes. GLM-4 can be run locally using frameworks like Ollama or vLLM. Petronella Technology Group builds GPU-accelerated workstations and servers optimized for local AI model deployment.

What GPU do I need for GLM-4?

The recommended GPU depends on the model size and quantization. For smaller quantized versions, an AMD Radeon or NVIDIA RTX GPU with 16-24 GB VRAM may suffice. For full-precision or larger variants, enterprise GPUs like the AMD Instinct MI300X or NVIDIA A100 are recommended.

Does Petronella help deploy GLM-4?

Yes. Petronella Technology Group provides end-to-end AI deployment services including hardware selection, system configuration, model optimization, and ongoing support. Contact us to discuss your GLM-4 deployment needs.

Open-Source AI Model

GLM-4

Name: GLM-4
Author: Zhipu AI (THUDM)

Developed by Zhipu AI (THUDM)

Local AI Deployment Experts 24+ Years IT Infrastructure GPU Hardware In Stock

Key Capabilities

Strong Chinese language understanding and generation
128K context window
Multi-modal capabilities (GLM-4V for vision)
Tool use and function calling
Web browsing and code interpreter built-in

VRAM Requirements by Quantization

Choose the right GPU based on your performance and quality needs.

Model / Quantization	VRAM Required
9B FP16	18GB
9B Q4	6GB

Use Cases

GLM-4 (9B (open), larger versions via API) can be deployed for enterprise AI applications including document processing, code generation, data analysis, and conversational AI. License: Apache 2.0 (GLM-4-9B).

Run GLM-4 with Petronella

PTG deploys GLM-4 for organizations needing Chinese-first AI capabilities with multimodal support. Small enough for edge deployment, powerful enough for production Chinese NLP tasks.

Recommended Hardware

Model Size	Recommended GPU
9B FP16	RTX 5080 (16GB) or RTX PRO 4000 (24GB)
9B Q4	Any GPU with 8GB+ VRAM

Deploy GLM-4 On-Premises

Our team builds GPU-accelerated systems configured and optimized for GLM-4. Private, secure, and fully under your control.

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GLM-4

⚡Key Capabilities

📌VRAM Requirements by Quantization

🚀Use Cases