Technical Analysis of Ultra-Large-Scale Parameter and Long-Context Models
The evolution of large language models has shifted from merely scaling parameters to integrating ultra-large parameter sizes, ultra-long context windows, and complex agent capabilities. This article provides a technical analysis of next-generation models with trillions of parameters and million-token context windows. It covers core architectural features like Mixture of Experts (MoE), advanced application scenarios in software engineering and 3D generation, inference performance optimization strategies including speculative decoding, and best practices for enterprise deployment using cloud-native architectures.
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