GLM-4 Local Install Guide 2026: Windows, Mac, Linux & Mobile
Quick Summary: Zhipu GLM-4 is a powerful 9-billion-parameter bilingual language model (Chinese + English) that runs smoothly on consumer hardware. With Ollama, you can have GLM-4 running locally in under 5 minutes. This guide covers installation on Windows, macOS, Linux, Android, and iOS, plus advanced configuration for developers who want to integrate GLM-4 into their applications.
What Is GLM-4?
GLM-4 is the fourth-generation General Language Model from Zhipu AI, a leading Chinese AI research laboratory affiliated with Tsinghua University. Released in 2024 and widely adopted throughout 2025–2026, GLM-4 represents a significant leap in bilingual language model capability — delivering near-frontier performance in both Chinese and English from a 9-billion-parameter model that fits comfortably on consumer GPUs.
What sets GLM-4 apart from other open-source 9B models is its specialized architecture for Chinese-English bilingual tasks. While models like Llama achieve Chinese capability through extensive multilingual training data, GLM-4 was architecturally designed with Chinese as a first-class language from the ground up. The result is noticeably better Chinese text quality, more accurate Chinese idiom understanding, and superior handling of mixed Chinese-English documents compared to Western-origin models of similar size.
GLM-4 also features a 128K token context window, enabling it to process full-length documents, long conversation histories, and entire codebases in a single pass. This is rare at the 9B parameter scale — most models this size are limited to 8K or 32K tokens.
GLM-4 is released under the Apache 2.0 license for the 9B version, making it fully usable in commercial applications. The model is available on HuggingFace as THUDM/glm-4-9b-chat and is also listed on the Ollama model library under the glm4 tag, making local installation straightforward for all skill levels.
Hardware Requirements
| Quantization | Min VRAM | Min RAM (CPU) | Speed (GPU) | Quality |
|---|---|---|---|---|
| FP16 (full) | 18GB+ | 32GB | 30–50 t/s | Best |
| Q8_0 | 10GB | 16GB | 25–45 t/s | Near-lossless |
| Q4_K_M | 6GB | 12GB | 20–40 t/s | Very Good |
| Q3_K_M | 4GB | 8GB | 15–30 t/s | Good |
Ideal Hardware
GLM-4 runs well on a single RTX 3060 12GB or any GPU with 8GB+ VRAM. Apple Silicon (M1/M2/M3) handles GLM-4 extremely well thanks to unified memory — a MacBook Pro M3 Pro achieves 30+ tokens/second on GLM-4:9b with Q4 quantization. Even older GPUs like the GTX 1080 Ti (11GB) deliver usable performance. For CPU-only inference, 16GB RAM is sufficient for Q4 quantization.
Install with Ollama (Fastest Method — All Platforms)
Ollama is by far the quickest way to get GLM-4 running. The same commands work on Windows, macOS, and Linux. First install Ollama, then pull and run GLM-4 in one command:
Windows
Download OllamaSetup.exe from ollama.com and run as Administrator. NVIDIA CUDA acceleration is automatic.
# After installing Ollama:
ollama run glm4
macOS
Download Ollama.dmg from ollama.com or install via Homebrew. Apple Silicon gets Metal GPU acceleration automatically.
brew install ollama
ollama serve &
ollama run glm4
Linux
One-command install covers Ubuntu, Debian, Fedora, Arch, and more. AMD ROCm users need ROCm drivers first.
curl -fsSL \
https://ollama.com/install.sh \
| sh
ollama run glm4
# GLM-4 Ollama commands reference:
ollama run glm4 # Download and start chat (default Q4)
ollama pull glm4 # Download only (no chat session)
ollama run glm4 "你好！请用中文回答我" # Chinese-language query
ollama run glm4 "Explain quantum computing in simple terms"
# Set longer context window (default is 2K, GLM-4 supports 128K):
ollama run glm4 --num-ctx 32768
# Check running models:
ollama list
After running ollama run glm4, Ollama will download the Q4_K_M quantized model (approximately 5.5GB) and launch an interactive chat session. GLM-4 performs well in both Chinese and English from the first prompt — you can freely mix languages mid-conversation.
LM Studio — Graphical Interface Setup
For users who prefer a ChatGPT-style interface without using the terminal, LM Studio offers a polished graphical application available on Windows and macOS. LM Studio can load GLM-4 in GGUF format directly from HuggingFace:
Download and Install LM Studio
Visit lmstudio.ai and download LM Studio for your platform (Windows .exe or macOS .dmg). Install normally. LM Studio bundles its own CUDA/Metal runtime, so no separate GPU libraries are needed.
Search for GLM-4 GGUF
Open LM Studio and click the magnifying glass (Discover). Search for "glm-4-9b" or "THUDM/glm-4". You'll see multiple quantization options. Select Q4_K_M for the best balance of quality and speed on 8GB VRAM, or Q8_0 if you have 12GB+ VRAM for near-lossless quality.
Load and Chat
Click the download button (cloud icon) to download the model. After download completes, go to the Chat tab, select your GLM-4 model from the top dropdown, and start chatting. Enable GPU Offload in settings to ensure your GPU is being used — move the slider to maximum to offload all layers to GPU for best performance.
LM Studio as Local API Server
LM Studio can act as an OpenAI-compatible API server. Go to the Server tab in LM Studio, select your loaded GLM-4 model, and click Start Server. This exposes GLM-4 at http://localhost:1234/v1 — you can then use it with any OpenAI SDK client by setting the base URL to your local address.
Python Direct Integration
For developers who want direct Python access to GLM-4 without Ollama or LM Studio, the HuggingFace Transformers library provides full control. This approach supports fine-tuning, custom system prompts, and batch inference:
# Install dependencies
pip install transformers accelerate bitsandbytes
# Load and run GLM-4 with 4-bit quantization
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
tokenizer = AutoTokenizer.from_pretrained(
"THUDM/glm-4-9b-chat", trust_remote_code=True
)
model = AutoModelForCausalLM.from_pretrained(
"THUDM/glm-4-9b-chat",
load_in_4bit=True, # Use 4-bit quant to save VRAM
device_map="auto",
trust_remote_code=True
)
inputs = tokenizer("Explain Python generators:", return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Open WebUI — Browser Chat Interface
Once Ollama is running GLM-4, Open WebUI provides a polished browser-based chat interface similar to ChatGPT. It works on all platforms where Docker is available:
docker run -d -p 3000:8080 \
--add-host=host.docker.internal:host-gateway \
-v open-webui:/app/backend/data \
--name open-webui \
ghcr.io/open-webui/open-webui:main
Navigate to http://localhost:3000, select glm4:latest from the model dropdown, and start chatting. Open WebUI supports conversation history, system prompts, file uploads, and multiple simultaneous model windows — perfect for comparing GLM-4's Chinese and English responses side by side.
Android Installation
GLM-4's 9B size (5.5GB in Q4) makes it too large for on-device inference on most Android phones, but there are excellent options for accessing it remotely or through API clients:
Remote Ollama Access
Run GLM-4 on your home PC via Ollama, then access it from your Android phone over Wi-Fi. Start Ollama with network binding: OLLAMA_HOST=0.0.0.0 ollama serve on your PC. Then use Enchanted for Android or any OpenAI-compatible chat app, pointing to your PC's local IP address on port 11434.
Zhipu API Client Apps
Zhipu AI (GLM's creator) provides an official API through open.bigmodel.cn. Get a free API key and configure any Android OpenAI-compatible app (such as TypingMind or ChatHub) with the Zhipu API endpoint: https://open.bigmodel.cn/api/paas/v4. This gives full cloud-backed GLM-4 access on your Android device.
PocketPal AI (On-Device, Smaller Models)
PocketPal AI on Google Play supports GGUF models including smaller GLM variants. For Android phones with 8GB+ RAM, the GLM-4-9B in Q2 quantization (~3GB) may run at 2–5 tokens/second. This is suitable for simple Chinese text tasks where the convenience of on-device inference outweighs the speed penalty. Download PocketPal from Google Play, tap the download icon, and search for GLM-4.
iPhone / iPad Installation
iOS users have excellent options for running GLM-4, both via API connection and for on-device inference on more recent, powerful Apple Silicon devices:
Enchanted App + Mac Bridge (Recommended)
Install Enchanted (free, App Store) on your iPhone. Configure your Mac's Ollama server to accept network connections by setting OLLAMA_HOST=0.0.0.0. Open Enchanted, go to Settings, enter your Mac's IP address and port 11434 as the Ollama server. Select GLM-4 as your model and enjoy full-quality inference with your Mac's GPU handling the heavy lifting. Response times of 1–2 seconds per token are typical on a Mac Mini M4 over local Wi-Fi.
PocketPal AI (On-Device, iPhone 15 Pro+)
iPhone 15 Pro with 8GB RAM can run GLM-4 in Q3 or Q4 quantization via PocketPal AI. Download PocketPal from the App Store, browse to the GLM-4-9B GGUF model, and download. On the A17 Pro chip, expect 8–15 tokens/second — usable for casual Chinese conversation and bilingual translation tasks. Ideal for users who need offline Chinese AI capability on their iPhone.
Zhipu Web Interface (iPad)
On iPad, the Zhipu AI web interface at chatglm.cn provides a polished tablet-optimized experience for the full cloud-hosted GLM model. No installation required — sign in with your Zhipu account and access GLM-4 (and newer models) through Safari or Chrome. The web interface supports long conversation history, file uploads, and the full 128K context window.
GLM-4 Performance Benchmarks
GLM-4's benchmark performance is impressive for a 9B parameter model, particularly on Chinese language tasks where it consistently outperforms larger Western-origin models:
MMLU-Pro scores. GLM-4 leads all 9B models on Chinese tasks, while remaining competitive in English with models from leading Western labs.
| Device | GPU/CPU | Speed (t/s) | Quantization |
|---|---|---|---|
| MacBook Pro M3 Pro | GPU+Neural | 30–45 t/s | Q4_K_M |
| RTX 3060 12GB | CUDA | 35–55 t/s | Q4_K_M |
| RTX 4090 24GB | CUDA | 60–90 t/s | FP16 |
| iPhone 15 Pro | A17 Pro | 8–15 t/s | Q4 |
| AMD RX 7900 XTX | ROCm | 25–40 t/s | Q4_K_M |
Troubleshooting
Problem: Slow download from Ollama or HuggingFace
Fix: GLM-4's model files are hosted on servers that can be slow to access from certain regions. Enable VPN07 before downloading — with 1000Mbps bandwidth and optimized routing, VPN07 dramatically speeds up model downloads. The GLM-4 Q4 model (~5.5GB) should download in 1–2 minutes with VPN07 enabled versus 20+ minutes without it from restricted regions.
Problem: Chinese characters display as garbled text
Fix: This is a terminal encoding issue on Windows, not a model problem. Open Windows Terminal (not old CMD) and ensure your terminal uses UTF-8 encoding. Run chcp 65001 before using Ollama, or switch to Windows Terminal which handles UTF-8 natively. For LM Studio, ensure your Windows locale is set to support Unicode under Region Settings.
Problem: Out-of-memory errors with 8GB GPU
Fix: The default Ollama quantization may be too large for 8GB VRAM when using longer context. Try: ollama run glm4 --num-ctx 4096 to reduce context size, or use ollama run glm4:q3_k_m for a smaller quantization. Alternatively, close other GPU-using applications before starting Ollama to free up VRAM.
Problem: Responses in wrong language
Fix: GLM-4 follows the language of your prompt by default. If you prompt in English, it responds in English. To force Chinese responses, use a Chinese prompt or add a system prompt: ollama run glm4 --system "请始终用中文回答". For the Transformers Python library, set the system message in the conversation template to specify your preferred response language.
Best Use Cases for GLM-4
🌐 Chinese-English Translation and Localization
GLM-4 produces translation quality that frequently outperforms dedicated translation APIs for nuanced business content. Its deep understanding of both Chinese and English cultural contexts means it correctly translates idioms, business terminology, and technical language rather than producing word-for-word literal translations. Particularly useful for software localization, marketing content, and technical documentation workflows.
💻 Code Development with Chinese Comments
Developers working in bilingual (Chinese + English) codebases find GLM-4 invaluable. It can read Chinese code comments and documentation, generate code with proper Chinese technical comments, and translate Chinese requirement documents into English code specifications. The 128K context means GLM-4 can hold an entire project's worth of files during a code review session.
📊 Business Analysis and Report Generation
GLM-4 excels at analyzing Chinese-language market reports, company filings, and news articles. Load an entire quarterly report (hundreds of pages) into GLM-4's 128K context, ask it to extract key metrics and generate an executive summary, then have it translate that summary to English for international stakeholders — all in a single workflow without losing context between steps.
GLM-4 Setup Checklist
Frequently Asked Questions
Q: Is GLM-4 better than Llama 3 for Chinese tasks?
Yes, consistently. GLM-4 was designed from the ground up for bilingual Chinese-English use, and it significantly outperforms Llama 3.1-8B on Chinese language benchmarks. For mixed Chinese-English workflows, GLM-4 is the recommended choice at the 9B parameter scale. For English-only tasks, Llama 3.1-8B and Phi-4-14B offer strong competition.
Q: Can I fine-tune GLM-4 on my own data?
Yes — GLM-4 supports LoRA and QLoRA fine-tuning through the official GLM repository on GitHub (github.com/THUDM/ChatGLM-6B). For most use cases, fine-tuning on a single RTX 4090 with 24GB VRAM is feasible with 4-bit quantization. The Apache 2.0 license permits fine-tuning for commercial applications.
Q: How does GLM-4 handle code generation?
GLM-4 is solid for code generation, particularly for Python, Java, and JavaScript. It's especially strong when the coding task involves Chinese-language specifications or documentation. For pure code generation benchmarks, Phi-4 and DeepSeek Coder offer competitive results, but GLM-4's advantage is its bilingual code understanding — it can read Chinese comments, specifications, and error messages accurately.
VPN07 — Download GLM-4 at Full Speed
1000Mbps · 70+ Countries · Trusted Since 2015
GLM-4 model files are hosted on Chinese servers (HuggingFace mirrors and Zhipu CDN). Download speeds without a VPN can be frustratingly slow from many regions. VPN07's 1000Mbps bandwidth and Asia-optimized server network delivers full-speed downloads wherever you are. The GLM-4 Q4 model (~5.5GB) downloads in under 2 minutes with VPN07. Trusted by developers in 70+ countries for over 10 years. $1.5/month with a 30-day money-back guarantee.
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