How AI Got Smaller And Still Became Smarter

Understanding how modern AI models are getting smaller while increasing efficiency is key to optimizing your tech stack. This video breaks down the technical shifts behind current development. We examine the relationship between token counts, cache size, and active parameters to clarify how efficiency gains are achieved. Whether you are curious about hardware constraints or software optimization, this analysis offers a clear view of the changing landscape. We specifically look at metrics like the Qwen harness and OPUS public numbers to contextualize what 9x more thinking actually means for real-world usage. By comparing these abstract data points, you will see how developers balance model performance against the practical reality of running AI cost effectively. Subscribe for weekly AI breakdowns, and let me know in the comments which model architecture you want me to analyze next. Timestamps: 00:00 - The 17GB AI Model Trap 02:59 - Active vs Total Parameters 04:10 - The Hidden Cost of 262K Context 06:02 - Does Qwen Really Beat Claude Opus? 07:42 - The “6B Parameters” Model That Needs 111GB 10:38 - Why Reasoning Can Cost So Much 12:10 - What Model Size Actually Means Related Videos: Deepseeks's New v4.1 Flash -    • Why DeepSeek Ditched Its Own Flagship (And...   What is Cost to Run Deepseek V4.1 Flash Locally -    • What It Actually Costs to Run DeepSeek V4....   #AI #LLM #LocalAI #OpenSourceAI #MachineLearning