Model reference · open weights

GLM-4.7-Flash

LLMs zai-org Text gen 3 builds Open weights 2M dl/mo

GLM-4.7-Flash is an open-weight language model from zai-org. GLM-4.7-Flash (BF16) weighs 62.4 GB; the smallest configuration that runs it is H100 80 GB.

For self-hosting, compare this build's requirements below with the GPU you plan to rent.

  • GLM-4.7-Flash is a 31.2B parameter text-generation model developed by zai-org.
  • It supports English and Chinese with a context length of 202,752 tokens and is released under the MIT license.
  • The model is designed for lightweight deployment and supports local inference via frameworks such as vLLM and SGLang.

Summary of the zai-org/GLM-4.7-Flash model card, 2026-10-01

What it is

Released byzai-org
Released2026-01-19
Parameters31.2B
VRAM62.4 GB for the weights

What it runs on

Memory and cards for GLM-4.7-Flash (BF16)

62.4 GBweights, file size
54 MBcache per 1K tokens
1.0 GBruntime overhead, at least
202,752 tokenscontext max
CardRequests at onceContext maxMemory
8K each32K each
RTX 3060 12 GB … L40S 48 GB
6 smaller cards
A100 80 GB338all 198K78.2 GB
H100 80 GB215169K78.1 GB
RTX PRO 6000 Blackwell 96 GB5614all 198K93.8 GB
DGX Spark (GB10) 128 GB unified8721all 198K107 GB
H200 141 GB15639all 198K138 GB
B200 180 GB23859all 198K176 GB
2× L40S 48 GB
tensor parallel
266all 198K44.0 GB a card
4× RTX 4090 24 GB
tensor parallel
153120K23.4 GB a card
4× RTX 3090 24 GB
tensor parallel
153121K23.4 GB a card
4× RTX 5090 32 GB
tensor parallel
328all 198K31.0 GB a card
2× H100 80 GB
tensor parallel
9122all 198K78.1 GB a card
Memory needed at each load
Requests at once8K tokens each32K tokens each
163.9 GB65.3 GB
565.7 GB72.4 GB
867.0 GB77.7 GB
1670.6 GB91.9 GB
3277.7 GB120 GB
6491.9 GB177 GB

One card, with vLLM's small-card settings.

Builds

Sizes, precisions & builds

BuildParamsPrecisionWeightsSmallest setup
GLM-4.7-Flash (above) ↗ 31.2BBF16 62.4 GBH100 80 GB
GLM-4.7-Flash ↗
packaged by unsloth
31.2BBF16 62.4 GBH100 80 GB
GLM-4.7-Flash-GGUF ↗
packaged by unsloth
28 builds: TQ1_0 8.3 GB … BF16 59.9 GB
GGUF 18.3 GB2× RTX 3060 12 GB

From the model card

What zai-org says about GLM-4.7-Flash

Read the model card

📖 Check out the GLM-4.7 technical blog, technical report(GLM-4.5). 📍 Use GLM-4.7-Flash API services on Z.ai API Platform. 👉 One click to GLM-4.7.

Introduction

GLM-4.7-Flash is a 30B-A3B MoE model. As the strongest model in the 30B class, GLM-4.7-Flash offers a new option for lightweight deployment that balances performance and efficiency.

Performances on Benchmarks

BenchmarkGLM-4.7-FlashQwen3-30B-A3B-Thinking-2507GPT-OSS-20B
AIME 2591.685.091.7
GPQA75.273.471.5
LCB v664.066.061.0
HLE14.49.810.9
SWE-bench Verified59.222.034.0
τ²-Bench79.549.047.7
BrowseComp42.82.2928.3

Evaluation Parameters

Default Settings (Most Tasks)

  • temperature: 1.0
  • top-p: 0.95
  • max new tokens: 131072

For multi-turn agentic tasks (τ²-Bench and Terminal Bench 2), please turn on Preserved Thinking mode.

Terminal Bench, SWE Bench Verified

  • temperature: 0.7
  • top-p: 1.0
  • max new tokens: 16384

τ^2-Bench

  • Temperature: 0
  • Max new tokens: 16384

For τ^2-Bench evaluation, we added an additional prompt to the Retail and Telecom user interaction to avoid failure modes caused by users ending the interaction incorrectly. For the Airline domain, we applied the domain fixes as proposed in the Claude Opus 4.5 release report.

Serve GLM-4.7-Flash Locally

For local deployment, GLM-4.7-Flash supports inference frameworks including vLLM and SGLang. Comprehensive deployment instructions are available in the official Github repository.

vLLM and SGLang only support GLM-4.7-Flash on their main branches.

vLLM

  • using pip (must use pypi.org as the index url):
pip install -U vllm --pre --index-url https://pypi.org/simple --extra-index-url https://wheels.vllm.ai/nightly
pip install git+https://github.com/huggingface/transformers.git

SGLang

  • Install the supported versions of SGLang and Transformers (using uv is recommended):
uv pip install sglang==0.3.2.dev9039+pr-17247.g90c446848 --extra-index-url https://sgl-project.github.io/whl/pr/
uv pip install git+https://github.com/huggingface/transformers.git@76732b4e7120808ff989edbd16401f61fa6a0afa

transformers

using with transformers as

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

and then run:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

MODEL_PATH = "zai-org/GLM-4.7-Flash"
messages = [{"role": "user", "content": "hello"}]
tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
inputs = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_dict=True,
    return_tensors="pt",
)
model = AutoModelForCausalLM.from_pretrained(
    pretrained_model_name_or_path=MODEL_PATH,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
inputs = inputs.to(model.device)
generated_ids = model.generate(**inputs, max_new_tokens=128, do_sample=False)
output_text = tokenizer.decode(generated_ids[0][inputs.input_ids.shape[1]:])
print(output_text)

vLLM

vllm serve zai-org/GLM-4.7-Flash \
     --tensor-parallel-size 4 \
     --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-4.7-flash

SGLang

python3 -m sglang.launch_server \
  --model-path zai-org/GLM-4.7-Flash \
  --tp-size 4 \
  --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 0.8 \
  --served-model-name glm-4.7-flash \
  --host 0.0.0.0 \
  --port 8000
  • For Blackwell GPUs, include --attention-backend triton --speculative-draft-attention-backend triton in your SGLang launch command.

Citation

If you find our work useful in your research, please consider citing the following paper:

@misc{5team2025glm45agenticreasoningcoding,
      title={GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models},
      author={GLM Team and Aohan Zeng and Xin Lv and Qinkai Zheng and Zhenyu Hou and Bin Chen and Chengxing Xie and Cunxiang Wang and Da Yin and Hao Zeng and Jiajie Zhang and Kedong Wang and Lucen Zhong and Mingdao Liu and Rui Lu and Shulin Cao and Xiaohan Zhang and Xuancheng Huang and Yao Wei and Yean Cheng and Yifan An and Yilin Niu and Yuanhao Wen and Yushi Bai and Zhengxiao Du and Zihan Wang and Zilin Zhu and Bohan Zhang and Bosi Wen and Bowen Wu and Bowen Xu and Can Huang and Casey Zhao and Changpeng Cai and Chao Yu and Chen Li and Chendi Ge and Chenghua Huang and Chenhui Zhang and Chenxi Xu and Chenzheng Zhu and Chuang Li and Congfeng Yin and Daoyan Lin and Dayong Yang and Dazhi Jiang and Ding Ai and Erle Zhu and Fei Wang and Gengzheng Pan and Guo Wang and Hailong Sun and Haitao Li and Haiyang Li and Haiyi Hu and Hanyu Zhang and Hao Peng and Hao Tai and Haoke Zhang and Haoran Wang and Haoyu Yang and He Liu and He Zhao and Hongwei Liu and Hongxi Yan and Huan Liu and Huilong Chen and Ji Li and Jiajing Zhao and Jiamin Ren and Jian Jiao and Jiani Zhao and Jianyang Yan and Jiaqi Wang and Jiayi Gui and Jiayue Zhao and J

Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.

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