Model reference · open weights
GLM-4.7-Flash is an open-weight language model from zai-org, listed in the AxForge catalogue. AxForge can bring it up on EU-owned hardware for you on request — with the licence handled where one is required.
About
GLM-4.7-Flash 👋 Join our <a href="https://discord.gg/QR7SARHRxK" target="blank"Discord</a community. 📖 Check out the GLM-4.7 <a href="https://z.ai/blog/glm-4.7" target="blank"technical blog</a, <a href="https://arxiv.org/abs/2508.06471" target="blank"technical report(GLM-4.5)</a. 📍 Use GLM-4.7-Flash API services on <a href="https://docs.z.ai/guides/llm/glm-4.7"Z.ai API Platform. </a 👉 One click to <a href="https://chat.z.ai"GLM-4.7</a. 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 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): SGLang + Install the supported versions of SGLang and Transformers (using uv is recommended): transformers using with transformers as and then run: vLLM SGLang + 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:
Summarised from the published model card. Read the full card on the HuggingFace links below.
Specifications
| Maker | zai-org |
|---|---|
| Type | Language models |
| Parameters (lead) | 31.2B |
| Context | 198k tokens |
| Variants | 1 |
| Runs with | transformers |
| Released | 2026-01-19 |
| Popularity | 2M downloads / month |
| Likes | 1,828 |
| Licence | Open weights |
How it works
Variants
Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.
| Variant | Params | Precision | VRAM | Fits 16 GB | Weights |
|---|---|---|---|---|---|
| GLM-4.7-Flash | 31.2B | BF16 | ~71.8 GB | — | Weights ↗ |
Using it via the API
Once AxForge deploys glm-4-7-flash for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (glm-4-7-flash below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/chat/completions \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"glm-4-7-flash","messages":[{"role":"user","content":"Hello"}]}'
Licence
Open weights under mit — commercial use is permitted. Deploy it on AxForge EU hardware on request. Read the licence ↗