GLM-5: 744B Sparse Model with 40B Active Parameters
GLM-5 is a 744-billion parameter sparse language model that activates only 40 billion parameters per forward pass, achieving efficient performance through
Someone noticed that Zhipu AI just dropped GLM-5, a massive sparse model that’s pretty interesting for anyone working on complex automation tasks.
The specs are wild:
- 744B total parameters (40B active at once)
- Trained on 28.5T tokens
- Uses DeepSeek Sparse Attention to keep costs reasonable
The sparse setup means it only activates 40B parameters per forward pass instead of loading the whole 744B model, which cuts deployment costs without killing performance on long-context work.
Quick links to check it out:
- Blog: https://z.ai/blog/glm-5
- Hugging Face: https://huggingface.co/zai-org/GLM-5
- GitHub: https://github.com/zai-org/GLM-5
Turns out it’s specifically built for “long-horizon agentic tasks” - basically stuff where an AI needs to plan multiple steps ahead. Could be handy for complex coding projects or multi-step system engineering problems.
Related Tips
20B Parameter Model Runs Locally in Browser
A 20 billion parameter AI language model has been successfully optimized to run entirely within a web browser, enabling local deployment without requiring
30B Model Handles 10M Tokens via Subquadratic Attention
A 30-billion parameter language model achieves 10-million token context processing through novel subquadratic attention mechanisms, dramatically reducing
KaniTTS2: Fast Local Text-to-Speech with Cloning
KaniTTS2 provides a fast, locally-run text-to-speech system with voice cloning capabilities, enabling users to generate natural-sounding speech from text while