GLM-4 9B
Zhipu's GLM-4 at 9B. Strong on Chinese-language tasks; tool-calling format slightly different from OpenAI convention.
Positioning
GLM-4 9B is a dense 9-billion-parameter model from Zhipu AI, released under the GLM License. With a 131K token context window, it is designed for Chinese-language tasks and tool-calling agents. Its architecture is dense, meaning all parameters are active during inference, making it straightforward to deploy on consumer hardware. The model's tool-calling format differs from the OpenAI convention, which may require adaptation for existing workflows.
Strengths
- Large context window: 131K tokens enables processing of long documents or multi-turn conversations without truncation.
- Strong Chinese-language performance: Built by Zhipu AI, the model is optimized for Chinese tasks, making it a strong choice for Chinese-language applications.
- Consumer-friendly size: At 9B dense parameters, the model fits on a single consumer GPU at common quantizations, enabling local deployment.
- Tool-calling focus: Designed for agentic workflows, with native support for tool use, though with a non-standard format.
Limitations
- Non-standard tool-calling format: The tool-calling convention differs from OpenAI's, requiring custom integration code.
- License restrictions: The GLM License may impose limitations on commercial use or redistribution; review terms carefully.
- Limited community benchmarks: We do not have verified community benchmark results for this model; vendor-reported metrics should be treated as best-case.
- Dense architecture: Unlike MoE models, all 9B parameters are active per token, so inference cost is proportional to full parameter count.
What it takes to run this locally
At FP16, the model requires ~18 GB of disk space. Quantized versions reduce this: Q8_0 ~10 GB, Q6_K ~7.4 GB, Q5_K_M ~6.4 GB, Q4_K_M ~5.1 GB, Q3_K_M ~4.4 GB, Q2_K ~2.9 GB. Add 30–50% for KV cache and framework overhead at typical context lengths. This places the model in the consumer deployment class: a single 12–24 GB GPU (e.g., RTX 3090/4090) can run Q4_K_M or Q5_K_M comfortably, while Q8_0 or FP16 may require a 24 GB card.
Should you run this locally?
Yes if: You need a strong Chinese-language model for local tool-calling agents, and you have a consumer GPU with at least 12 GB VRAM. The large context window is valuable for processing long Chinese documents.
No if: Your workflow relies on OpenAI-compatible tool-calling formats, or you require a permissive license for unrestricted commercial deployment. Also, if your tasks are primarily English, other models may be more suitable.
Catalog cross-links
- GLM-4 9B Chat
- Zhipu AI
- Consumer GPU Guide
Overview
Zhipu's GLM-4 at 9B. Strong on Chinese-language tasks; tool-calling format slightly different from OpenAI convention.
Family & lineage
How this model relates to others in its lineage. Family members share architecture and training-data roots; parent / children edges record direct distillation or fine-tune relationships.
Strengths
- Chinese-language depth
- Strong tool-calling
Weaknesses
- Restricted license
- Custom tool-call format
Quantization variants
Each quantization trades model quality for file size and VRAM. Q4_K_M is the most popular starting point.
| Quantization | File size | VRAM required |
|---|---|---|
| Q4_K_M | 5.5 GB | 8 GB |
Get the model
HuggingFace
Original weights
Source repository — direct quantization required.
Hardware that runs this
Cards with enough VRAM for at least one quantization of GLM-4 9B.
Models worth comparing
Same parameter band, plus what's one tier above and below — so you can decide what actually fits your hardware.
Frequently asked
What's the minimum VRAM to run GLM-4 9B?
Can I use GLM-4 9B commercially?
What's the context length of GLM-4 9B?
Source: huggingface.co/THUDM/glm-4-9b-chat
Reviewed by RunLocalAI Editorial. See our editorial policy for how we research and verify model claims.
Related — keep moving
Verify GLM-4 9B runs on your specific hardware before committing money.