How to Run GLM-5 Locally: Complete Step-by-Step Guide

Introduction

GLM-5 is the latest open-source large language model from Z.ai, featuring 744B total parameters (40B active) with MoE architecture. This powerful model excels at reasoning, coding, and agentic tasks, making it one of the best open-source LLMs available today.

Running GLM-5 locally gives you full control over your data, eliminates API costs, and allows for unlimited usage. In this guide, we'll walk you through the complete process of setting up and running GLM-5 locally on your hardware.

Why Run GLM-5 Locally?

Benefit	Description
Data Privacy	Your data never leaves your system
Cost Savings	No API fees or usage limits
Customization	Fine-tune for your specific needs
Unlimited Usage	Generate as much as you want
No Latency	Fast responses without network calls

Hardware Requirements

Before running GLM-5 locally, ensure your system meets these requirements:

Minimum Requirements

Component	Minimum	Recommended
GPU	4x NVIDIA A100 (40GB)	8x NVIDIA H100/A100 (80GB)
VRAM	160GB	320GB+
RAM	64GB	128GB+
Storage	500GB SSD	1TB+ NVMe SSD
CUDA	11.8	12.0+

Note: GLM-5 uses a Mixture-of-Experts (MoE) architecture with 40B active parameters, making it more efficient than dense models of similar size.

Method 1: Running GLM-5 Locally with vLLM

vLLM is one of the fastest and most popular LLM serving frameworks, offering high throughput and low latency.

Step 1: Install vLLM

Using Docker (Recommended):

docker pull vllm/vllm-openai:nightly

Using pip:

pip install -U vllm --pre \
  --index-url https://pypi.org/simple \
  --extra-index-url https://wheels.vllm.ai/nightly

Step 2: Install Required Dependencies

pip install git+https://github.com/huggingface/transformers.git
pip install torch

Step 3: Start the GLM-5 Server

vllm serve zai-org/GLM-5-FP8 \
  --tensor-parallel-size 8 \
  --gpu-memory-utilization 0.85 \
  --speculative-config.method mtp \
  --speculative-config.num_speculative_tokens 1 \
  --tool-call-parser glm47 \
  --reasoning-parser glm45 \
  --enable-auto-tool-choice \
  --served-model-name glm-5-fp8 \
  --host 0.0.0.0 \
  --port 8000

Parameter Explanation:

Parameter	Purpose
tensor-parallel-size 8	Distribute across 8 GPUs
gpu-memory-utilization 0.85	Use 85% of GPU memory
speculative-config.method mtp	Enable speculative decoding
tool-call-parser glm47	Parse tool calls
reasoning-parser glm45	Parse reasoning content

Step 4: Test Your GLM-5 Installation

Create a test script test_glm5.py:

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:8000/v1",
    api_key="token-not-required"
)

response = client.chat.completions.create(
    model="glm-5-fp8",
    messages=[
        {"role": "user", "content": "Hello! How are you?"}
    ],
    temperature=0.7,
    max_tokens=512
)

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

Run it:

python test_glm5.py

Method 2: Running GLM-5 Locally with SGLang

SGLang is optimized specifically for GLM-5 and offers excellent performance.

Step 1: Pull Docker Image

# For Hopper GPUs (A100, H100)
docker pull lmsysorg/sglang:glm5-hopper

# For Blackwell GPUs
docker pull lmsysorg/sglang:glm5-blackwell

Step 2: Launch GLM-5 Server

python3 -m sglang.launch_server \
  --model-path zai-org/GLM-5-FP8 \
  --tp-size 8 \
  --tool-call-parser glm47 \
  --reasoning-parser glm45 \
  --speculative-algorithm EAGLE \
  --speculative-num-steps 3 \
  --speculative-eagle-topk 1 \
  --speculative-num-draft-tokens 4 \
  --mem-fraction-static 0.85 \
  --served-model-name glm-5-fp8 \
  --host 0.0.0.0 \
  --port 30000

Step 3: Interact with GLM-5

import openai

client = openai.OpenAI(
    base_url="http://localhost:30000/v1",
    api_key="token-not-required"
)

response = client.chat.completions.create(
    model="glm-5-fp8",
    messages=[{"role": "user", "content": "Write a Python function to sort a list."}],
    max_tokens=512
)

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

Method 3: Running GLM-5 with Hugging Face Transformers

For simple inference tasks, use Transformers directly.

Step 1: Install Transformers

pip install transformers torch accelerate

Step 2: Load and Run GLM-5

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Load model and tokenizer
model_name = "zai-org/GLM-5"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.float16,
    device_map="auto"
)

# Prepare input
messages = [
    {"role": "user", "content": "Explain machine learning in simple terms."}
]

# Generate response
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=2048,
    temperature=0.7,
    top_p=0.95
)

# Decode response
generated_ids = [
    output_ids[len(input_ids):]
    for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

GLM-5 Use Cases

After running GLM-5 locally, here are some practical ways to use it:

1. Coding Assistant

GLM-5 achieves 77.8% on SWE-bench Verified, making it excellent for:

• Code generation and completion
• Bug detection and fixing
• Code refactoring
• Technical documentation

prompt = "Write a Python function to implement a REST API with Flask"
# Send to GLM-5...

2. Mathematical Reasoning

With 92.7% on AIME 2026 and 96.9% on HMMT, GLM-5 excels at:

• Mathematical problem-solving
• Scientific research
• Financial modeling
• Engineering calculations

3. Agentic Tasks

GLM-5 scores 56.2% on Terminal-Bench 2.0 and 75.9% on BrowseComp, perfect for:

• Automated workflows
• Command-line operations
• Web browsing and research
• Tool integration

4. Multilingual Applications

With strong English and Chinese support (72.7% on BrowseComp-Zh):

• Translation services
• Cross-lingual content creation
• Multilingual customer support
• Language learning

5. Enterprise Applications

• Document analysis and summarization
• Knowledge base querying
• Technical writing assistance
• Compliance checking

6. Research and Development

• Literature review
• Hypothesis generation
• Experimental design
• Data analysis

Running GLM-5 Locally vs. Cloud VPS

If you don't have powerful enough hardware to run GLM-5 locally, consider using a cloud GPU VPS:

Option	Pros	Cons
Local Machine	Full privacy, no ongoing costs	High upfront hardware cost
Cloud VPS	No hardware investment, scalable	Monthly fees, data sent to cloud

Cloud VPS Solution: LightNode

For those without suitable local hardware, LightNode offers excellent GPU VPS solutions for running GLM-5:

Why LightNode?

Feature	Benefit
Global Locations	Deploy close to users
GPU Support	8x A100/H100 instances available
Pay-as-you-go	Hourly billing
Easy Setup	Pre-configured GPU images

Recommended LightNode Configurations

Configuration	Use Case	Monthly Cost*
8x A100 (80GB)	Production deployment	~$400-800
4x A100 (80GB)	Development & testing	~$200-400
8x A40 (48GB)	Budget option	~$300-600

*Estimated cost, actual pricing may vary

Quick Setup on LightNode

1. Create an account at LightNode
2. Select a GPU instance (8x A100 recommended for GLM-5)
3. Choose your region (closest to you for lowest latency)
4. Install Docker and vLLM:

   sudo apt update
   curl -fsSL https://get.docker.com | sh
   docker pull vllm/vllm-openai:nightly

5. Start GLM-5:

   docker run --gpus all -it --rm \
     -p 8000:8000 \
     vllm/vllm-openai:nightly \
     serve zai-org/GLM-5-FP8 \
     --tensor-parallel-size 8 \
     --gpu-memory-utilization 0.85

Optimization Tips for Running GLM-5 Locally

1. Use FP8 Quantization

# Load FP8 quantized model
vllm serve zai-org/GLM-5-FP8 ...

2. Enable Speculative Decoding

Speculative decoding can improve throughput by up to 2x:

--speculative-config.method mtp \
--speculative-config.num_speculative_tokens 5

3. Adjust GPU Memory

--gpu-memory-utilization 0.90  # Increase if you have more VRAM

4. Batch Multiple Requests

# Send multiple requests in one batch
responses = client.chat.completions.create(
    model="glm-5-fp8",
    messages=[
        [{"role": "user", "content": "Query 1"}],
        [{"role": "user", "content": "Query 2"}],
    ]
)

Troubleshooting

Out of Memory Error

# Reduce batch size or GPU memory utilization
--gpu-memory-utilization 0.70

Slow Inference

# Enable speculative decoding
--speculative-config.method mtp
--speculative-config.num_speculative_tokens 5

Connection Refused

# Check if the server is running
curl http://localhost:8000/health

# Check firewall settings
sudo ufw allow 8000/tcp

Official Resources

• Hugging Face Model: https://huggingface.co/zai-org/GLM-5
• GitHub Repository: https://github.com/zai-org/GLM-5
• Z.ai Documentation: https://docs.z.ai/guides/llm/glm-5
• Technical Blog: https://z.ai/blog/glm-5
• Discord Community: Join

Conclusion

Running GLM-5 locally gives you access to one of the most powerful open-source LLMs available, with complete control over your data and no API limitations. Whether you choose vLLM, SGLang, or direct Transformers integration, the setup process is straightforward once you have the right hardware.

If local hardware is a constraint, LightNode provides affordable GPU VPS options that make running GLM-5 accessible to everyone. With global locations and flexible pricing, you can deploy GLM-5 in minutes.

Start running GLM-5 locally today and unlock the full potential of open-source AI!

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Need GPU resources to run GLM-5? Check out LightNode for affordable GPU VPS solutions.