Model reference · open weights
GLM-4.7-Flash is an open-weight language model from zai-org. GLM-4.7-Flash-GGUF (GGUF) weighs 18.3 GB; the smallest configuration that runs it is 2× RTX 3060 12 GB.
For self-hosting, compare this build's requirements below with the GPU you plan to rent.
AxForge offerings: GLM-4.7-Flash
Summary of the zai-org/GLM-4.7-Flash model card, 2026-10-01. The estimate below is for another build of the family.
What it is
| Released by | zai-org |
|---|---|
| Released | 2026-01-19 |
| Parameters | 31.2B |
| VRAM | 18.3 GB for the weights |
What it runs on
How much memory each request adds isn't estimated yet for this architecture. The weights need at least the cards below, plus room for the context.
| Card | Weights alone |
|---|---|
| RTX 3060 12 GB … RTX 4060 Ti 16 GB 2 smaller cards | does not fit |
| RTX 3090 24 GB | fits |
| RTX 4090 24 GB | fits |
| RTX 5090 32 GB | fits |
| L40S 48 GB | fits |
| A100 80 GB | fits |
| H100 80 GB | fits |
| RTX PRO 6000 Blackwell 96 GB | fits |
| DGX Spark (GB10) 128 GB unified | fits |
| H200 141 GB | fits |
| B200 180 GB | fits |
| 2× RTX 3060 12 GB split by layers (llama.cpp) | fits |
| 2× RTX 4060 Ti 16 GB split by layers (llama.cpp) | fits |
Builds
| Build | Params | Precision | Weights | Smallest setup |
|---|---|---|---|---|
| GLM-4.7-Flash ↗ | 31.2B | BF16 | 62.4 GB | H100 80 GB |
| GLM-4.7-Flash ↗ packaged by unsloth |
31.2B | BF16 | 62.4 GB | H100 80 GB |
| GLM-4.7-Flash-GGUF (above) ↗ packaged by unsloth 28 builds: TQ1_0 8.3 GB … BF16 59.9 GB |
GGUF | 18.3 GB | 2× RTX 3060 12 GB |
From 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.
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.
| Benchmark | GLM-4.7-Flash | Qwen3-30B-A3B-Thinking-2507 | GPT-OSS-20B |
|---|---|---|---|
| AIME 25 | 91.6 | 85.0 | 91.7 |
| GPQA | 75.2 | 73.4 | 71.5 |
| LCB v6 | 64.0 | 66.0 | 61.0 |
| HLE | 14.4 | 9.8 | 10.9 |
| SWE-bench Verified | 59.2 | 22.0 | 34.0 |
| τ²-Bench | 79.5 | 49.0 | 47.7 |
| BrowseComp | 42.8 | 2.29 | 28.3 |
Default Settings (Most Tasks)
1.00.95131072For multi-turn agentic tasks (τ²-Bench and Terminal Bench 2), please turn on Preserved Thinking mode.
Terminal Bench, SWE Bench Verified
0.71.016384τ^2-Bench
016384For 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.
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.
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
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
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 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
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
--attention-backend triton --speculative-draft-attention-backend triton in your SGLang launch command.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 JQuoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.