https://github.com/zai-org/GLM-5
GLM-5: From Vibe Coding to Agentic Engineering
https://github.com/zai-org/GLM-5
agentic-ai coding glm llm
Last synced: 23 days ago
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GLM-5: From Vibe Coding to Agentic Engineering
- Host: GitHub
- URL: https://github.com/zai-org/GLM-5
- Owner: zai-org
- License: apache-2.0
- Created: 2026-02-09T08:17:02.000Z (about 2 months ago)
- Default Branch: main
- Last Pushed: 2026-02-12T07:13:55.000Z (about 1 month ago)
- Last Synced: 2026-02-12T16:45:41.627Z (about 1 month ago)
- Topics: agentic-ai, coding, glm, llm
- Homepage: https://z.ai/blog/glm-5
- Size: 3.2 MB
- Stars: 423
- Watchers: 2
- Forks: 32
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# GLM-5
👋 Join our Wechat or Discord community.
📖 Check out the GLM-5 technical blog.
📍 Use GLM-5 API services on Z.ai API Platform.
👉 One click to GLM-5.
## Introduction
We are launching GLM-5, targeting complex systems engineering and long-horizon agentic tasks. Scaling is still one of the most important ways to improve the intelligence efficiency of Artificial General Intelligence (AGI). Compared to GLM-4.5, GLM-5 scales from 355B parameters (32B active) to 744B parameters (40B active), and increases pre-training data from 23T to 28.5T tokens. GLM-5 also integrates DeepSeek Sparse Attention (DSA), largely reducing deployment cost while preserving long-context capacity.
Reinforcement learning aims to bridge the gap between competence and excellence in pre-trained models. However, deploying it at scale for LLMs is a challenge due to the RL training inefficiency. To this end, we developed [slime](https://github.com/THUDM/slime), a novel **asynchronous RL infrastructure** that substantially improves training throughput and efficiency, enabling more fine-grained post-training iterations. With advances in both pre-training and post-training, GLM-5 delivers significant improvement compared to GLM-4.7 across a wide range of academic benchmarks and achieves best-in-class performance among all open-source models in the world on reasoning, coding, and agentic tasks, closing the gap with frontier models.
GLM-5 is purpose-built for complex systems engineering and long-horizon agentic tasks. On our internal evaluation suite CC-Bench-V2, GLM-5 significantly outperforms GLM-4.7 across frontend, backend, and long-horizon tasks, narrowing the gap to Claude Opus 4.5.
On [Vending Bench 2](https://andonlabs.com/evals/vending-bench-2), a benchmark that measures long-term operational capability, GLM-5 ranks \#1 among open-source models. Vending Bench 2 requires the model to run a simulated vending machine business over a one-year horizon; GLM-5 finishes with a final account balance of $4,432, approaching Claude Opus 4.5 and demonstrating strong long-term planning and resource management.
## Download Model
| Model | Download Links | Model Size | Precision |
|-----------|---------------------------------------------------------------------------------------------------------------------------------|------------|-----------|
| GLM-5 | [🤗 Hugging Face](https://huggingface.co/zai-org/GLM-5)
[🤖 ModelScope](https://modelscope.cn/models/ZhipuAI/GLM-5) | 744B-A40B | BF16 |
| GLM-5-FP8 | [🤗 Hugging Face](https://huggingface.co/zai-org/GLM-5-FP8)
[🤖 ModelScope](https://modelscope.cn/models/ZhipuAI/GLM-5-FP8) | 744B-A40B | FP8 |
## Serve GLM-5 Locally
### Prepare environment
vLLM, SGLang, and xLLM all support local deployment of GLM-5. A simple deployment guide is provided here.
+ vLLM
Using Docker as:
```shell
docker pull vllm/vllm-openai:nightly
```
or using pip:
```shell
pip install -U vllm --pre --index-url https://pypi.org/simple --extra-index-url https://wheels.vllm.ai/nightly
```
then upgrade transformers:
```
pip install git+https://github.com/huggingface/transformers.git
```
+ SGLang
Using Docker as:
```bash
docker pull lmsysorg/sglang:glm5-hopper # For Hopper GPU
docker pull lmsysorg/sglang:glm5-blackwell # For Blackwell GPU
```
### Deploy
+ vLLM
```shell
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
```
Check the [recipes](https://github.com/vllm-project/recipes/blob/main/GLM/GLM5.md) for more details.
+ SGLang
```shell
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
```
Check the [sglang cookbook](https://cookbook.sglang.io/autoregressive/GLM/GLM-5) for more details.
+ xLLM and other Ascend NPU
Please check the deployment guide [here](https://github.com/zai-org/GLM-5/blob/main/example/ascend.md).
## Citation
Our technical report is coming soon.