Run a 32B AI model locally for coding, Q&A, and tool-calling tasks
Serve GLM models via vLLM for production inference workloads
Use the smaller 9B model on limited hardware for math and reasoning tasks
Generate long research reports with the Rumination variant that searches the web during thinking
| zai-org/glm-4 | arcee-ai/mergekit | vt-vl-lab/3d-photo-inpainting | |
|---|---|---|---|
| Stars | 7,076 | 7,077 | 7,073 |
| Language | Python | Python | Python |
| Setup difficulty | hard | moderate | hard |
| Complexity | 4/5 | 4/5 | 4/5 |
| Audience | developer | researcher | researcher |
Figures from each repo's GitHub metadata at analysis time.
Requires a GPU with sufficient VRAM, the 32B model needs significant hardware, the 9B is the minimum for most consumer GPUs.
GLM-4 is a family of open-source AI chat models built by the team at Zhipu AI. The models are designed to understand and generate text in multiple languages and can also work with images and other non-text inputs. The project contains several model variants released in April 2025, each aimed at different use cases and hardware constraints. The main model, GLM-4-32B-0414, has 32 billion parameters and was trained on 15 trillion tokens of text data. According to the README, its performance on coding and question-answering benchmarks is comparable to GPT-4o and DeepSeek-V3, both of which are substantially larger. It handles tasks like writing and running code, calling external tools or APIs, answering questions from web search, and generating structured documents. A second variant, GLM-Z1-32B-0414, adds extended reasoning. It was trained with extra steps focused on mathematics, logic, and code so it can work through harder multi-step problems before giving an answer. A third variant, GLM-Z1-Rumination-32B-0414, goes further: it is designed for longer, more open-ended tasks like writing detailed research reports. It can search the web during its thinking process to gather information before composing a response. For users with limited computing resources, there is a smaller 9-billion-parameter option called GLM-Z1-9B-0414. The README notes it ranks near the top of open-source models at that size, particularly for math reasoning, making it a practical choice when running a full 32B model is not feasible. The repository includes Python code for running inference, notebooks demonstrating specific capabilities, and instructions for using vLLM to serve the models in production. Deployment guides for Ollama and llama.cpp are also provided. The models are hosted on Hugging Face and can be downloaded freely. A commercial API version is available at bigmodel.cn, and the models can be tested without downloading anything at chat.z.ai.
GLM-4 is a family of open-source AI chat models by Zhipu AI with 9B and 32B parameter variants for text generation, coding, reasoning, tool use, and writing long research reports.
Mainly Python. The stack also includes Python, PyTorch, vLLM.
Setup difficulty is rated hard, with roughly 1h+ to a first successful run.
Mainly developer.
This repo across BitVibe Labs
Verify against the repo before relying on details.