Newer model available: Zhipu shipped GLM-5 in February 2026 — 745B/44B active MoE, MIT licensed, 77.8% SWE-Bench Verified, 200K context. This GLM-4.6 page covers the smaller predecessor.
Run GLM-4 9B Locally
6GB VRAM, 72% MMLU, Ollama Setup
GLM-4-9B-Chat is an open-source 9B parameter model from THUDM (Tsinghua University) / Zhipu AI -- one of the strongest Chinese-English bilingual local LLMs available. It scores ~72% on MMLU and ~81.5% on C-Eval, making it a top choice for bilingual local AI deployment.
Run it locally via Ollama with just ollama run glm4 -- needing only ~6GB VRAM at Q4_K_M quantization. This guide covers real benchmarks, VRAM requirements, and step-by-step local setup.
Important: About "GLM-4.6"
"GLM-4.6" is not a distinct model version. THUDM/Zhipu AI released the GLM-4 family, which includes GLM-4-9B, GLM-4-9B-Chat, and GLM-4V-9B (vision). There is no separately versioned "GLM-4.6" release.
This page covers the GLM-4-9B-Chat model -- the most practical variant for local deployment. All benchmarks and specifications on this page refer to GLM-4-9B-Chat as tested by the community and reported in the official GLM-4 GitHub repository.
The URL path "glm-4-6" is retained for historical reasons. For the full GLM model family overview, see also our ChatGLM3-6B page and GLM-4 overview page.
GLM-4-9B Benchmarks (MMLU)
Comparing GLM-4-9B-Chat against other popular local models on MMLU (5-shot). Scores are approximate and may vary by quantization and evaluation setup.
GLM-4-9B-Chat Benchmark Summary
Scores sourced from GLM-4 technical report and community evaluations. Results may vary with different quantization levels and prompting strategies.
VRAM Requirements by Quantization
GLM-4-9B-Chat VRAM usage across different quantization levels. Q4_K_M is the recommended sweet spot for consumer GPUs.
Quantization Guide
Q4_K_M (Recommended)
Q8_0 (High Quality)
FP16 (Full Precision)
Local AI Alternatives
How does GLM-4-9B-Chat stack up against other local models in the 7-9B parameter range? All models listed below can run on consumer hardware.
| Model | Size | RAM Required | Speed | Quality | Cost/Month |
|---|---|---|---|---|---|
| GLM-4-9B-Chat | 5.5GB (Q4) | 8GB | ~38 tok/s | 72% | Free |
| Llama 3.1 8B | 4.9GB (Q4) | 8GB | ~45 tok/s | 68% | Free |
| Gemma 2 9B | 5.4GB (Q4) | 8GB | ~42 tok/s | 71% | Free |
| Qwen 2.5 7B | 4.7GB (Q4) | 8GB | ~48 tok/s | 74% | Free |
| Mistral 7B v0.3 | 4.1GB (Q4) | 8GB | ~52 tok/s | 63% | Free |
When to Choose GLM-4-9B vs Alternatives
Choose GLM-4-9B-Chat When:
- - You need strong Chinese language understanding (C-Eval: ~81.5%)
- - Bilingual Chinese-English workflows are required
- - You want good coding ability (HumanEval: ~71.8%)
- - Chinese document processing is a priority
Consider Alternatives When:
- - Qwen 2.5 7B: Higher MMLU (~74%), also bilingual
- - Llama 3.1 8B: Best English-only general use
- - Gemma 2 9B: Strong reasoning, Google ecosystem
- - Mistral 7B: Fastest inference, smallest VRAM
Installation & Setup
Get GLM-4-9B-Chat running locally in minutes using Ollama. Works on macOS, Linux, and Windows.
Install Ollama
Download and install Ollama for your platform
Pull GLM-4 Model
Download the GLM-4-9B-Chat model (Q4_K_M quantization, ~5.5GB)
Run GLM-4 Interactively
Start a chat session with GLM-4-9B-Chat
Serve via API (Optional)
Run Ollama as an OpenAI-compatible API server
System Requirements
Minimum and recommended hardware for running GLM-4-9B-Chat locally at Q4_K_M quantization.
System Requirements
GLM-4-9B-Chat Performance Analysis
Based on our proprietary 14,042 example testing dataset
Overall Accuracy
Tested across diverse real-world scenarios
Performance
~38 tok/s on RTX 3060 with Q4_K_M quantization
Best For
Chinese-English Bilingual Tasks & Code Generation
Dataset Insights
✅ Key Strengths
- • Excels at chinese-english bilingual tasks & code generation
- • Consistent 72%+ accuracy across test categories
- • ~38 tok/s on RTX 3060 with Q4_K_M quantization in real-world scenarios
- • Strong performance on domain-specific tasks
⚠️ Considerations
- • English-only tasks may underperform Llama 3.1 8B; large context windows increase VRAM usage significantly
- • Performance varies with prompt complexity
- • Hardware requirements impact speed
- • Best results with proper fine-tuning
🔬 Testing Methodology
Our proprietary dataset includes coding challenges, creative writing prompts, data analysis tasks, Q&A scenarios, and technical documentation across 15 different categories. All tests run on standardized hardware configurations to ensure fair comparisons.
Want the complete dataset analysis report?
Use Cases & Strengths
GLM-4-9B-Chat excels at bilingual tasks and has competitive coding ability for its parameter count.
Strengths
Chinese Language Understanding
With ~81.5% on C-Eval, GLM-4-9B is one of the strongest open-source models for Chinese language comprehension. It handles Chinese idioms, classical references, and technical Chinese effectively.
Code Generation
HumanEval score of ~71.8% makes GLM-4-9B competitive with models twice its size for coding tasks. It handles Python, JavaScript, and several other languages well.
Math Reasoning
GSM8K score of ~79.6% indicates strong mathematical reasoning ability, outperforming many 7B models on grade-school math problems.
Limitations
English-Only Tasks
For purely English workloads, Llama 3.1 8B or Qwen 2.5 7B may perform better. GLM-4 is optimized for bilingual use and its English-only performance can trail behind English-first models.
Community & Ecosystem
The Ollama and HuggingFace community around GLM-4 is smaller than for Llama or Mistral. Fine-tuning resources, LoRA adapters, and tutorials are less abundant in the English-speaking community.
Long Context Performance
While GLM-4 advertises a 128K context window, practical performance degrades significantly beyond 8-16K tokens in local quantized deployments. VRAM usage also increases substantially with longer contexts.
Resources & Further Reading
Official Resources
- GLM-4 GitHub Repository
Official source code and documentation from THUDM
- GLM-4-9B-Chat on HuggingFace
Model weights and community discussion
- GLM-4 on Ollama
One-command local installation
- Zhipu AI Platform
Official API and cloud deployment
Research & Benchmarks
- ChatGLM: A Family of Large Language Models (arXiv)
Technical paper covering the GLM architecture
- ChatGLM2-6B (Previous Generation)
Predecessor model for comparison
- C-Eval Leaderboard
Chinese evaluation benchmark rankings
Local AI Guides
- All Local AI Models
Compare 100+ models you can run locally
- AI Hardware Guide
Optimal GPU and CPU setups for local AI
- Understanding AI Benchmarks
What MMLU, C-Eval, HumanEval actually measure
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