How to Download GLM 5.2: Open Weights, API Access, and Local Deployment

GLM 5.2 — officially named GLM-4-Plus and developed by Zhipu AI (智谱 AI) — is one of the most capable large language models available as of mid-2025. With a 1-million-token context window, strong coding and reasoning benchmarks, and native Chinese-English bilingual support, it has attracted significant interest from developers who want to run it locally or integrate it into their applications.

This guide covers every path to using GLM 5.2: what you can actually download, the best open-weight variants for local deployment, step-by-step setup instructions, and how to access the full-scale 753B model via API when local hardware isn't an option.

Understanding the GLM 5.2 Model Family

Before downloading anything, it's important to understand what "GLM 5.2" refers to across different contexts:

• GLM-4-Plus (GLM 5.2 API model): The flagship proprietary model — a 753B total / 40B active Mixture-of-Experts architecture. This model is only available via Zhipu AI's API (glm-4-plus at https://open.bigmodel.cn/api/paas/v4/). No local download exists for the full 753B weights.
• GLM-4-9B-Chat: The open-weight model from the same generation, released under an MIT license. Available on HuggingFace, this 9B-parameter model is the primary option for local deployment.
• GLM-4-9B-Chat-1M: A long-context variant of GLM-4-9B supporting up to 1 million tokens.
• GLM-Z1 series: A reasoning-focused model series also available as open weights, aimed at complex multi-step problem solving.
• GLM-4V: The vision-capable variant supporting image understanding alongside text.

If your goal is to reproduce the full performance of GLM 5.2 locally, the honest answer is: you can't — the full model isn't publicly distributed. What you can download is GLM-4-9B-Chat, which delivers solid performance at consumer-grade hardware requirements, or use the API for full-scale access.

Option 1: Download GLM-4-9B-Chat (Open Weights, MIT License)

GLM-4-9B-Chat is the recommended route for anyone wanting a locally deployed GLM model. The weights are hosted on HuggingFace under THUDM/glm-4-9b-chat and are free to download and use commercially under the MIT license.

Step 1: Install the HuggingFace Hub CLI

pip install huggingface_hub

Step 2: Log In to HuggingFace

Some model repos require authentication. Log in once with your HuggingFace account token:

huggingface-cli login

Paste your access token when prompted. You can generate one at huggingface.co/settings/tokens.

Step 3: Download the Model Weights

huggingface-cli download THUDM/glm-4-9b-chat --local-dir ./glm-4-9b-chat

This will download all model shards, tokenizer files, and configuration into the ./glm-4-9b-chat directory. Expect around 18–20 GB of disk space for the full-precision weights.

For the long-context variant:

huggingface-cli download THUDM/glm-4-9b-chat-1m --local-dir ./glm-4-9b-chat-1m

Option 2: Run Locally with Hugging Face Transformers

Once downloaded, you can run inference using the transformers library. GLM-4-9B-Chat supports the standard AutoModelForCausalLM interface.

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_path = "./glm-4-9b-chat"

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
)
model.eval()

messages = [
    {"role": "user", "content": "Explain the difference between MoE and dense transformer architectures."}
]

inputs = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt"
).to(model.device)

with torch.no_grad():
    outputs = model.generate(inputs, max_new_tokens=512, do_sample=False)

response = tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True)
print(response)

Hardware requirements: For bfloat16 inference, you'll need roughly 20 GB of GPU VRAM. A single NVIDIA RTX 4090 (24 GB) or two RTX 3090s (24 GB each) is typically sufficient. For CPU-only or mixed CPU/GPU setups, see the quantized options below.

Option 3: Run with vLLM for High-Throughput Inference

If you're serving the model to multiple users or need high throughput, vLLM is the recommended engine. It supports GLM-4-9B-Chat through its standard API.

pip install vllm

python -m vllm.entrypoints.openai.api_server \
  --model ./glm-4-9b-chat \
  --dtype bfloat16 \
  --trust-remote-code \
  --port 8000

Once running, query it with any OpenAI-compatible client:

curl http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "glm-4-9b-chat",
    "messages": [{"role": "user", "content": "Hello, what can you do?"}]
  }'

Option 4: Quantized GGUF Versions with llama.cpp or Ollama

For users without a dedicated GPU, quantized GGUF versions significantly reduce memory requirements. Community contributors on HuggingFace regularly publish GGUF quantizations of GLM-4-9B-Chat — search for glm-4-9b GGUF on HuggingFace to find current community-maintained versions. Availability and quality of community quantizations may vary, so check the model card and repo activity before downloading.

Using Ollama (Easiest Path)

If a GLM-4 GGUF is available in the Ollama model library:

# Install Ollama first: https://ollama.com/download
ollama pull glm4  # check https://ollama.com/library for current availability
ollama run glm4

Using llama.cpp Directly

# Build llama.cpp
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp && make

# Download a GGUF file (search HuggingFace for community quantizations)
# Then run:
./llama-cli -m glm-4-9b-chat.Q4_K_M.gguf \
  -p "You are a helpful assistant." \
  --ctx-size 4096 \
  -n 512

A Q4_K_M quantization of the 9B model typically requires around 6–8 GB of RAM, making it viable on most modern laptops.

Option 5: Access Full GLM 5.2 (753B) via API

For the full-scale GLM-4-Plus model — the one behind the benchmark scores of 89% on GPQA Diamond and 74.3% on LiveCodeBench — local deployment is not an option. The complete 753B MoE model is only available through Zhipu AI's API.

Pricing (per official documentation, verify at open.bigmodel.cn):

• Input: $1.40 per million tokens
• Output: $4.40 per million tokens

Getting Started with the API

1. Register at open.bigmodel.cn — new accounts receive free trial tokens.
2. Generate an API key from the console.
3. Install the SDK:

pip install zhipuai

4. Make your first call:

from zhipuai import ZhipuAI

client = ZhipuAI(api_key="your_api_key_here")

response = client.chat.completions.create(
    model="glm-4-plus",
    messages=[
        {"role": "user", "content": "Summarize the key differences between MoE and dense LLMs."}
    ],
    max_tokens=1024,
    temperature=0.7,
)

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

The API is OpenAI-compatible, so existing integrations using the openai Python client can often be pointed at GLM's endpoint with minimal changes.

Choosing the Right GLM 5.2 Deployment Path

Skipping the Download Entirely

If you want to evaluate GLM 5.2's full capabilities before committing to an API integration or local setup, glm5.app offers hosted access without any download, account setup, or configuration. It's the fastest way to test the model on your actual use cases — whether that's long-document analysis, code generation, or bilingual tasks.

For a deeper walkthrough of the API integration path, see our related guide on GLM 5.2 API access and authentication.

Troubleshooting Common Issues

"Trust remote code" errors: GLM-4-9B-Chat uses custom model code. Always pass trust_remote_code=True when loading with transformers. Only do this with code from trusted repositories.

Out of memory errors: If you run out of VRAM with bfloat16, try torch_dtype=torch.float16 or use a quantized GGUF version. You can also try device_map="auto" with multiple GPUs.

Slow download speeds: HuggingFace can be slow in some regions. Set the environment variable HF_ENDPOINT=https://hf-mirror.com to use a mirror (check current mirror availability in your region).

Chat template errors: GLM-4-9B-Chat uses a specific chat format. Always use tokenizer.apply_chat_template() rather than formatting messages manually.

Summary

GLM 5.2 (GLM-4-Plus) represents the current state of the art from Zhipu AI, but the full 753B model is API-only. For local deployment, GLM-4-9B-Chat is your best option — it's MIT-licensed, well-supported on HuggingFace, and runs on consumer hardware with appropriate quantization. For maximum capability without any hardware overhead, the Zhipu API or glm5.app will get you there immediately.

Keep in mind that the GLM model family continues to evolve. Zhipu AI regularly releases new checkpoints, reasoning variants (GLM-Z1), and vision-enabled models (GLM-4V), so it's worth watching the THUDM HuggingFace organization and the official open platform for updates. Whether you go the local route for privacy and cost control, or the API route for simplicity and full model capability, GLM 5.2 is a strong choice for both Chinese and English workloads.

Sources

• Zhipu AI Open Platform: https://open.bigmodel.cn
• THUDM on HuggingFace (GLM-4-9B-Chat and related models): https://huggingface.co/THUDM
• GLM-4-9B-Chat model card: https://huggingface.co/THUDM/glm-4-9b-chat
• vLLM documentation: https://docs.vllm.ai
• llama.cpp GitHub: https://github.com/ggerganov/llama.cpp
• Ollama: https://ollama.com
• ZhipuAI Python SDK: https://github.com/zhipuai/zhipuai-sdk-python-v4