Running a 744B AI Model on Regular Hardware The Problem Large AI models like GLM-5.2 have 744 billion parameters. These need huge amounts of memory - more than most computers have. These models normally require expensive servers that cost thousands of dollars. Our Solution: Colibri Colibri is a lightweight C program that runs massive AI models on regular computers with only 25 GB of RAM. The trick? It streams specialists from disk. How It Works (Simple Version) Think of a huge model as having many specialized "experts". Each expert is good at one thing: - Expert 1: Good at math - Expert 2: Good at writing - Expert 3: Good at coding For any question, only about 40 billion parameters are active (not all 744 billion). So: 1. Keep the most-used parts in RAM (about 10 GB) 2. Keep 19,456 specialized experts on disk 3. Load only the experts you need from disk (streaming) 4. Use a cache to keep hot experts ready Real Performance Numbers - Model size on disk: 370 GB (compressed to int4) - Memory used: 9.9 GB RAM - Load time: 30 seconds - Speed: 0.05-0.1 tokens/second on regular hardware (with warm cache, faster) - Can answer questions: Yes! Correctly! Why This Matters - Run 744B parameter models on a ~$500 computer - No GPU needed (optional GPU makes it faster) - Pure C code: one file, no dependencies - Shows that huge AI doesn't need huge servers The Honest Part It's not fast compared to $100k GPU clusters. But it works, and it proves you can run frontier-class AI on a regular machine. The tradeoff: disk reads are slow, but the model stays correct. Try It Download GLM-5.2 int4 model and run the chat interface. Web dashboard available too. This is the future of AI: efficient, accessible, and running on your machine.