Show HN: Getting GLM 5.2 running on my slow computer (github.com)

Article summary

The article presents a project called Colibri, which allows running the 744B-parameter GLM-5.2 model on a consumer-grade computer with 25 GB of RAM. The model is optimized to use disk streaming and caching to reduce memory usage. The project provides a C-based engine and a Python converter, and it supports various features like speculative decoding and batch processing. The model can be run on Windows and Linux, and it has been tested on various hardware configurations.

Main themes

• Efficient model execution
• Disk streaming and caching
• Consumer-grade hardware
• Model optimization
• Cross-platform support

What commenters say

• The project demonstrates the hacker spirit of achieving remarkable performance on limited hardware.
• Some commenters question the practicality of running such a large model on consumer-grade hardware, citing concerns about SSD lifespan and performance.
• Others argue that the project's approach can be useful for applications where cloud services are not feasible due to privacy concerns or cost.
• There is a discussion about the potential for integrating the project with other tools and frameworks, such as ticketing systems and IDEs.
• Some commenters note that the project's performance may not be suitable for real-time applications, but it can still be useful for tasks that can be run overnight or in batches.
• The project's ability to run on a variety of hardware configurations, including older systems, is seen as a significant advantage by some commenters.