Inside 2025’s LLM Revolution: From GPT-2 to Kimi 2 Architectures Explained
23 days ago 高效码农From GPT-2 to Kimi 2: A Visual Guide to 2025’s Leading Large Language Model Architectures If you already use large language models but still get lost in technical jargon, this post is for you. In one long read you’ll learn: Why DeepSeek-V3’s 671 B parameters run cheaper than Llama 3’s 405 B How sliding-window attention lets a 27 B model run on a Mac Mini Which open-weight model to download for your next side project Table of Contents Seven Years of the Same Backbone—What Actually Changed? DeepSeek-V3 / R1: MLA + MoE, the Memory-Saving Duo OLMo 2: Moving RMSNorm One …
How Lightning Attention Slashes AI Inference Costs: The MiniMax-M1 Breakthrough Explained
1 months ago 高效码农MiniMax-M1: How Lightning Attention is Revolutionizing Large Model Inference Efficiency AI Chips and Light Trajectories Introduction: Breaking Through Traditional Transformer Efficiency Barriers In artificial intelligence, large model inference efficiency has become a critical bottleneck limiting technological advancement. The traditional Transformer architecture faces inherent limitations in long-sequence processing due to the quadratic computational complexity of its softmax attention mechanism. MiniMax’s newly released MiniMax-M1 model achieves unprecedented efficiency breakthroughs through innovative hybrid architecture while maintaining cutting-edge reasoning capabilities. The core of this technological breakthrough lies in lightning attention mechanism, combined with a Mixture-of-Experts (MoE) system, enabling the model to process million-token contexts …