Basic information
Model repository: zai-org/GLM-5.2
Organization: zai-org
Tasks: text-generation
Architecture and runtime
Model type: glm_moe_dsa
Architecture: GlmMoeDsaForCausalLM
Libraries: safetensors, pytorch
Base models: Not provided
Current revision: master
License and update history
License: mit
Created: 2026-06-16
Last updated: 2026-09-02
Hardware requirements
参考配置（非官方最低要求）：Hugging Face 元数据显示约 753.3B BF16 参数，权重约 1.51 TB（十进制）。需要多机多卡推理，建议合计显存超过 1.6 TB，并为 KV cache 和长上下文额外预留显存。参数换算是估算，并非官方最低配置；请按模型卡要求使用支持的 vLLM 版本。 Inference reference: official Hugging Face metadata lists about 753.3B BF16 parameters, or roughly 1.51 TB of weights (decimal). Distributed multi-GPU inference is required; allow over 1.6 TB aggregate VRAM plus KV-cache/context headroom. This is a parameter-based estimate, not an official minimum; use a vLLM version supported by the model card.
Download links
Basic usage example
启动 vLLM 多卡服务 / Start multi-GPU vLLM server
# Example for a multi-GPU host; size parallelism to the available hardware.
pip install -U "vllm>=0.23.0" openai
vllm serve zai-org/GLM-5.2 \
--tensor-parallel-size 16 \
--pipeline-parallel-size 2 \
--max-model-len 32768 \
--trust-remote-code调用 OpenAI 兼容接口 / Call the OpenAI-compatible API
from openai import OpenAI
client = OpenAI(api_key="EMPTY", base_url="http://localhost:8000/v1")
response = client.chat.completions.create(
model="zai-org/GLM-5.2",
messages=[{"role": "user", "content": "请用三点介绍这个模型的主要使用特点。"}],
max_tokens=512,
)
print(response.choices[0].message.content)