Local AI Just Hit the Tipping Point (Colibri)

Want to run a 744 billion parameter frontier AI model entirely on your own laptop for free? In this video we break down the system design behind running GLM-5.2 locally using Colibri a 1,300-line single-file C engine with no Python, no BLAS, no GPU required explaining exactly how only 40 billion parameters activate per token, how Colibri streams all 21,504 routed experts on demand from your NVMe SSD using a per-layer LRU cache, how MTP speculative decoding at int8 precision doubles token throughput, how the learning cache auto-pins your hottest experts into RAM so the model gets faster the longer you use it, and how a 25GB laptop can run the strongest open-weight model in the world GLM-5.2, which scores 62.1% on SWE-bench Pro beating GPT-5.5 at 58.6%, ranks 51 on the Artificial Analysis Intelligence Index (the highest any open model has ever scored), and ships under a fully permissive MIT license from a Tsinghua spinoff on the US entity list — all at a sixth of the cost of closed API alternatives, with full setup walkthrough included. 🔗 Colibri GitHub: https://github.com/jlnsrk/colibri 🔗 GLM-5.2 Hugging Face: https://huggingface.co/THUDM/GLM-5.2 🔗 Prebuilt int4 Checkpoint: https://huggingface.co/jlnsrk/GLM-5.2... 🔗 Zhipu AI Official: https://zhipuai.cn 🔗 SWE-bench Leaderboard: https://www.swebench.com 🔗 Artificial Analysis Index: https://artificialanalysis.ai #GLM52 #ZhipuAI #Colibri #LocalAI #RunAILocally #MixtureOfExperts #MoEModel #OpenSourceAI #LocalLLM #AIOnLaptop #FreeAI #SWEBench #AIEngineering #ai2026