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Are we officially entering the post-cloud era of Artificial Intelligence? In mid-2026, the Local AI ecosystem crossed a massive tipping point thanks to the collapse of three major walls: the quality gap (GLM-5.2 beating GPT-5.5), the memory wall, and the hardware monopoly (training on 100k Huawei chips with zero Nvidia GPUs). 🔔 Subscribe: https://www.youtube.com/channel/UC0DZj1PNa_Fp0MU6uPSKv5w?sub_confirmation=1 💙 Become a Member: https://www.youtube.com/channel/UC0DZj1PNa_Fp0MU6uPSKv5w/join 🐦 Twitter/X: https://x.com/cloud_codes 💬 Discord: https://discord.gg/HVnH9SY48 In this video, Cloud Codes breaks down the system design behind Colibri, an open-source C project that proves you no longer need a data center to run frontier AI. We explore how Colibri streams a massive 744B parameter Mixture-of-Experts (MoE) model straight from an SSD into just 25GB of RAM. We explain the genius of Unsloth's dynamic 2-bit quantization and why the true bottleneck for Local AI is now your PCIe 5 NVMe drive, not your GPU. Finally, we discuss the geopolitical and architectural reasons why this tipping point is irreversible, and why the future of AI belongs to open weights running on local hardware. 🔗 Resources & Tools Mentioned: • Colibri GitHub Repo: https://github.com/JustVugg/colibri ⏱️ TIMESTAMPS: 0:00 - The Impossible Demo (744B on a Laptop) 1:08 - Wall 1: The Quality Gap is Gone (GLM-5.2 vs GPT-5.5) 3:15 - Wall 2: Breaking the Memory Wall 4:36 - How Mixture-of-Experts (MoE) Makes It Possible 4:55 - Unsloth 2-Bit Dynamic Quantization 5:12 - Trick 3: Streaming Experts from the SSD 5:51 - Colibri: The Open Source C Engine 7:00 - The Honest Catch: 0.1 Tokens Per Second 7:58 - Wall 3: Bypassing the Nvidia Hardware Monopoly 8:51 - Why the Local AI Tipping Point is Irreversible 9:29 - Summary: The Death of the Cloud Data Center #localai #colibri #machinelearning #systemdesign #softwareengineering #artificialintelligence #cloudcodes #llm #hardware #glm #opensource User Queries: we just hit the local ai tipping point how to run glm-5.2 locally colibri colibri github repository tutorial run 700b parameter model on laptop mixture of experts ai architecture unsloth dynamic 2-bit quantization explained glm 5.2 vs gpt 5.5 swe bench why local ai is better than cloud ai stop paying for openai api local alternative how to run large language models offline
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