#大模型架构
5 related articles
Deep Dives·2 min
The "Worse is Better" Philosophy of Large Model Design: Why Simple and Brutal Beats Refined and Complex
Analyzing the "worse is better" philosophy in large model architecture: why DeepSeek V4 dropped N-gram, why Transformer dominates AI, and three iron laws of simple, efficient model design.
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Deep Dives·3 min
DeepSeek V4 Deep Technical Breakdown: Million-Token Context and Extreme Cost Efficiency
Deep analysis of DeepSeek V4's core architecture: Hybrid Compressed Attention, Manifold-Constrained Hyperconnection, and MUON optimizer—how they cut inference costs by 10x and enable million-token context processing.
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Deep Dives·4 min
Core Principles of the Transformer Architecture: A Deep Dive into Self-Attention Mechanisms and Engineering Optimizations
Deep dive into Transformer architecture covering self-attention QKV mechanics, Encoder-Decoder structure, Flash Attention memory optimization, RoPE positional encoding, and GQA inference acceleration.
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Deep Dives·1 min
Qwen3.5 Deep Dive: Hybrid Attention Architecture Achieves 19x Speedup for Long Context
Deep analysis of Alibaba's open-source Qwen3.5 hybrid attention architecture, how Gated Delta Net achieves 19x speedup at 256K context, and multimodal results surpassing Gemini 3 Pro and GPT-5.2.
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Deep Dives·3 min
ReAct Pattern and Transformer Architecture Explained: High-Frequency AI Interview Topics for Frontend Developers
Deep dive into Agent core logic ReAct (Reasoning + Acting) execution flow, Transformer self-attention mechanism, and essential AI skills for frontend developers including prompt engineering, RAG, and Agent development.
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