GLM-5 is an open-source AI model developed by Zhipu AI (ZAI). It can be deployed on-premises with the right GPU hardware for private, secure AI inference.

How much VRAM does GLM-5 require?

VRAM requirements for GLM-5 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-5 locally?

Yes. GLM-5 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-5?

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-5?

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-5 deployment needs.

Open-Source AI Model

GLM-5

Name: GLM-5
Author: Zhipu AI (ZAI)

Developed by Zhipu AI (ZAI)

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

Key Capabilities

744B parameters with only 40B active per token (MoE efficiency)
202K token context window with sparse attention
Best-in-class agentic engineering and systems tasks
Native tool calling with optimized parsers
SWE-bench Verified 77.8%, AIME 2026 92.7%, GPQA-Diamond 86.0%

VRAM Requirements by Quantization

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

Model / Quantization	VRAM Required
active FP16	80GB
full FP16	1.5TB
Q4	120GB

Use Cases

GLM-5 (744B total (40B active via MoE)) can be deployed for enterprise AI applications including document processing, code generation, data analysis, and conversational AI. License: MIT.

Run GLM-5 with Petronella

PTG deploys GLM-5 for enterprises needing frontier-class agentic AI under MIT license. 744B parameters with only 40B active delivers cost-effective inference for complex software engineering, security analysis, and systems automation tasks.

Recommended Hardware

Model Size	Recommended GPU
Q4	DGX Spark (128GB) or 2x RTX PRO 6000 (192GB)
FP16	DGX Station GB300 (384GB) or 4x RTX PRO 6000 (384GB)

Deploy GLM-5 On-Premises

Our team builds GPU-accelerated systems configured and optimized for GLM-5. Private, secure, and fully under your control.

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GLM-5

⚡Key Capabilities

📌VRAM Requirements by Quantization

🚀Use Cases