colibri - colibri | gitzw.com

Summary

Run GLM-5.2 (744B MoE) on a 25GB-RAM consumer machine — pure C, zero deps, experts streamed from disk. Tiny engine, immense model. 🐦

🤖 AI Deep Analysis

Colibri is a groundbreaking tool that democratizes access to massive MoE models by enabling inference on consumer hardware, though it sacrifices speed and flexibility for memory efficiency.

✅ Pros

Runs a massive 744B MoE model on consumer hardware with only 25GB RAM
Pure C implementation with zero dependencies
Streams experts from disk, enabling memory-efficient inference
Lightweight and tiny engine footprint
High performance due to low-level C optimization

⚠️ Cons

Limited to inference only, no training support
Requires downloading the large model weights separately
May have slower inference due to disk streaming
Lacks extensive documentation and community support
Not suitable for real-time or low-latency applications

🎯 Use cases

Running large language models on low-resource consumer hardware
Offline or batch inference tasks where latency is not critical
Educational purposes to understand MoE model inference
Prototyping and experimentation with GLM-5.2

⚖️ Comparison

Compared to llama.cpp, which also runs LLMs on consumer hardware, Colibri focuses on MoE models like GLM-5.2 and uses disk streaming to handle larger models. llama.cpp supports a wider range of models and has more features, but Colibri is more specialized for extremely large MoE models.