How to Use GLM for Free: Complete Guide to Accessing Zhipu AI's Language Models Without Cost

If you've been looking for free access to powerful language models, you're in the right place. Zhipu AI's GLM (General Language Model) series offers some of the most capable open-source models available today, and you can use them completely free of charge.

In this comprehensive guide, you'll learn:

• What GLM models are and why they're powerful
• Multiple ways to use GLM for free (API, local deployment, and more)
• Step-by-step setup instructions
• Code examples for various use cases
• How to optimize your setup for cost savings

What Is GLM?

GLM (General Language Model) is a series of large language models developed by Zhipu AI, a leading Chinese AI research company. The GLM models are:

• Open Source: Available under permissive licenses
• High Performance: Compete with GPT-3.5 and GPT-4 in many tasks
• Multilingual: Support multiple languages including Chinese, English, and more
• Versatile: Good for chat, coding, translation, summarization, and more

The latest GLM versions (such as GLM-4, GLM-4V, and specialized variants) offer:

• Advanced reasoning capabilities
• Long context windows
• Excellent code generation
• Multimodal understanding (text, images, etc.)

Why Use GLM for Free?

1. No API Costs

GLM models can be deployed locally, eliminating per-token costs.

2. Privacy and Control

Run everything on your own infrastructure with no data sent to external servers.

3. Customization

Fine-tune models on your own data for specific use cases.

4. Integration

Build custom applications with API-compatible interfaces.

5. Learning and Experimentation

Perfect for developers learning LLMs without budget constraints.

Method 1: Use GLM via Official API (Free Tier)

Zhipu AI provides a generous free tier for their GLM models, making it easy to get started without any setup.

Step 1: Sign Up and Get API Key

1. Visit Zhipu AI Developer Platform
2. Register for a free account
3. Navigate to "API Management" to get your API key

Step 2: Install the GLM SDK

pip install zhipuai

Step 3: Make Your First API Call

from zhipuai import ZhipuAI

# Initialize with your API key
client = ZhipuAI(api_key="YOUR_API_KEY")

# Call GLM-4 model
response = client.chat.completions.create(
    model="glm-4",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "What is the capital of France?"}
    ],
    temperature=0.7
)

print(response.choices[0].message.content)

Step 4: Monitor Your Free Credits

The free tier typically includes:

• 1,000,000 tokens per month
• Access to GLM-4 and GLM-4V models
• No commitment required

Visit your dashboard to track usage and credits.

Method 2: Local Deployment with vLLM (Completely Free)

For zero cost and full control, deploy GLM models locally using vLLM.

Prerequisites

• Minimum: 16GB RAM, Python 3.10+
• Recommended: 32GB+ RAM, NVIDIA GPU with 8GB+ VRAM
• For GLM-4: 64GB+ RAM or dedicated GPU

Step 1: Install vLLM

pip install vllm

Step 2: Download and Run GLM Model

python3 -m vllm.entrypoints.openai.api_server \
    --model THUDM/glm-4-9b-chat \
    --served-model-name glm-4-9b-chat \
    --port 8000

This will download the model (~18GB) and start a local API server.

Step 3: Use the Local Model

from openai import OpenAI

# Connect to your local server
client = OpenAI(
    base_url="http://localhost:8000/v1",
    api_key="EMPTY"  # vLLM uses empty key by default
)

response = client.chat.completions.create(
    model="glm-4-9b-chat",
    messages=[
        {"role": "user", "content": "Explain quantum computing in simple terms"}
    ]
)

print(response.choices[0].message.content)

Step 4: Multiple Model Options

You can run various GLM variants:

# GLM-4-9B-Chat (Chatbot optimized)
python3 -m vllm.entrypoints.openai.api_server \
    --model THUDM/glm-4-9b-chat \
    --served-model-name glm-4-9b-chat \
    --port 8000

# GLM-4-9B-Code (Code generation focused)
python3 -m vllm.entrypoints.openai.api_server \
    --model THUDM/glm-4-9b-code \
    --served-model-name glm-4-9b-code \
    --port 8000

# GLM-4-9B-Air (Lightweight version)
python3 -m vllm.entrypoints.openai.api_server \
    --model THUDM/glm-4-9b-air \
    --served-model-name glm-4-9b-air \
    --port 8000

Method 3: Use AutoGLM for Mobile Automation (Free)

If you want to use GLM to control your phone automatically, check out AutoGLM, the open-source mobile AI agent that uses GLM models.

See the complete guide here.

AutoGLM allows you to:

• Control your Android phone with natural language
• Automate repetitive tasks
• Test mobile applications
• Build AI-powered mobile workflows

Method 4: Use Ollama for Local GLM (Easy Setup)

Ollama provides an even easier way to run GLM locally with minimal setup.

Step 1: Install Ollama

macOS:

curl -fsSL https://ollama.com/install.sh | sh

Linux:

curl -fsSL https://ollama.com/install.sh | sh

Windows:
Download from https://ollama.com

Step 2: Pull and Run GLM Model

# Download GLM-4 model
ollama pull glm4

# Start the model server
ollama serve

Step 3: Use via API

import requests

response = requests.post(
    "http://localhost:11434/api/chat",
    json={
        "model": "glm4",
        "messages": [
            {"role": "user", "content": "What is machine learning?"}
        ]
    }
)

print(response.json()['message']['content'])

Best Practices for Free GLM Usage

1. Choose the Right Model

• For Development/Testing: Use smaller models (7B-9B parameters)
• For Production: Consider 9B+ models with more context
• For Code: Use specialized code variants
• For Chinese: Choose Chinese-optimized models

2. Optimize Token Usage

# Use system prompts effectively
response = client.chat.completions.create(
    model="glm-4",
    messages=[
        {
            "role": "system",
            "content": "You are a concise technical writer. Be direct and avoid fluff."
        },
        {"role": "user", "content": "Explain this complex concept..."}
    ]
)

3. Implement Caching

Cache common responses and prompts to reduce API calls.

4. Use Streaming for Better UX

stream = client.chat.completions.create(
    model="glm-4",
    messages=[...],
    stream=True
)

for chunk in stream:
    print(chunk.choices[0].delta.content, end="")

5. Batch Similar Requests

Combine multiple queries into a single API call when possible.

Real-World Use Cases

1. Personal Assistant

Build your own AI assistant that answers questions, sets reminders, and manages your schedule.

2. Content Generation

Create blog posts, social media content, marketing copy, and more.

3. Code Assistant

Get help with coding, debugging, and refactoring.

4. Translation Tool

Build a multilingual translation service.

5. Customer Support Bot

Create automated customer support agents for your business.

6. Learning Tool

Study languages, prepare for exams, or learn new concepts.

Comparison: Free GLM vs Paid APIs

Feature	Free GLM	Paid APIs (OpenAI, Anthropic)
Cost	$0 (local)	$0.002-$0.12 per 1K tokens
Privacy	Complete control	Data sent to provider
Speed	Local hardware	CDN-based
Customization	Full control	Limited fine-tuning
Rate Limits	Your hardware	Provider limits
Uptime	Your infrastructure	Provider SLA

Hardware Recommendations

CPU-Only Setup (16GB RAM)

• Use: GLM-4-9B-Air or smaller models
• Performance: 1-2 tokens/second
• Best for: Testing and development

Mid-Range Setup (32GB RAM, no GPU)

• Use: GLM-4-9B (quantized)
• Performance: 3-5 tokens/second
• Best for: Personal use, small projects

GPU Setup (NVIDIA 8GB+ VRAM)

• Use: GLM-4-9B-Chat (full precision)
• Performance: 20-50 tokens/second
• Best for: Production use

High-Performance Setup (GPU with 24GB+ VRAM)

• Use: GLM-4-9B or GLM-4-20B (if available)
• Performance: 50+ tokens/second
• Best for: Heavy production workloads

Troubleshooting Common Issues

Issue: Out of Memory

Solution: Use quantized models (int8 or int4) or smaller model sizes.

# Use quantization
python3 -m vllm.entrypoints.openai.api_server \
    --model THUDM/glm-4-9b-chat \
    --quantization awq \
    --port 8000

Issue: Slow Performance

Solution: Enable caching and use GPU acceleration.

# Enable GPU acceleration
python3 -m vllm.entrypoints.openai.api_server \
    --model THUDM/glm-4-9b-chat \
    --gpu-memory-utilization 0.9 \
    --port 8000

Issue: Connection Refused

Solution: Ensure the server is running and port is not blocked.

# Check if server is running
curl http://localhost:8000/v1/models

# Check port usage
netstat -an | grep 8000

Frequently Asked Questions

Is GLM completely free?

Yes, if you deploy it locally using vLLM or Ollama. The official API offers a generous free tier as well.

Which GLM model should I use?

For beginners, start with GLM-4-9B-Air. For production, try GLM-4-9B-Chat.

Can I run GLM on a laptop?

Yes, smaller GLM variants can run on laptops with 16GB+ RAM. CPU-only performance is slower but functional.

Does GLM support other languages?

Yes, GLM models are multilingual and excel at Chinese and English.

Can I fine-tune GLM?

Yes, you can fine-tune GLM models on your own data, though this requires substantial compute resources.

How do I deploy GLM for others to use?

Run the local server with firewall rules, then configure your applications to connect to it.

Conclusion

You now have multiple ways to use GLM for free:

1. Use the official API with free credits
2. Deploy locally with vLLM for complete control
3. Use AutoGLM for mobile automation
4. Use Ollama for easy setup

Each method has its advantages:

• API: Easiest setup, best for quick testing
• vLLM: Best performance, full customization
• AutoGLM: Unique mobile automation capabilities
• Ollama: Simplest installation process

Choose the method that fits your needs and start building amazing applications with GLM today!

---

Recommended Hosting for Running GLM Locally

If you plan to run GLM models 24/7 (for example, as an API service for your applications), you'll need reliable hosting. While you can run GLM locally, deploying it on a VPS offers several benefits:

• 24/7 availability without keeping your local machine running
• Remote access from anywhere
• Better performance with dedicated resources
• Scalability to handle multiple users

Why Choose LightNode VPS?

LightNode is an excellent choice for running GLM models because:

1. Hourly Billing

You only pay for the resources you use, which is perfect for:

• Testing different model sizes
• Development and experimentation
• Short-term projects
• Avoiding long-term commitments

2. Global Locations

Choose data centers close to your users for:

• Lower latency
• Better performance
• Compliance with regional data laws

3. Lightweight Resources

GLM models can run efficiently on:

• 2GB-4GB RAM instances
• CPU-based instances
• Budget-friendly pricing

4. Easy Setup

Quick deployment with:

• One-click marketplace images
• Pre-configured environments
• Developer-friendly tools

Recommended LightNode Configuration

For running GLM-4-9B locally:

Instance: c3.medium
CPU: 4 vCPU
RAM: 8 GB
Storage: 40 GB SSD
Network: 100 Mbps
Price: ~$5-10/month (hourly pricing applies)

This setup provides:

• Smooth model inference
• Support for multiple concurrent requests
• Enough RAM for efficient operation
• Ample storage for models and data

Getting Started with LightNode

1. Sign Up: Visit LightNode
2. Select Instance: Choose a configuration based on your needs
3. Launch: One-click deployment in under 60 seconds
4. Connect: Access via SSH or web console
5. Install GLM: Follow the vLLM setup guide
6. Start Serving: Your GLM API is ready!

Real-World Performance

Users report excellent performance with LightNode for:

• Personal AI assistants running 24/7
• Local LLM services for development teams
• API endpoints for web applications
• Testing and experimentation environments

The combination of affordable hourly pricing and reliable infrastructure makes LightNode ideal for both learning and production use cases.

Start your free trial today at LightNode and experience the power of free GLM models with reliable hosting!

---

Resources:

• Zhipu AI Official Site
• GLM GitHub Repository
• vLLM Documentation
• Ollama Website
• AutoGLM GitHub
• LightNode VPS