Development of Large Model Deployment Optimization Technology Under Edge-limited Conditions
ZHANG Mingxuan
Abstract：Large language models(LLMs),due to their large number of parameters and the need to handle long contextual in-formation,face significant challenges in deployment and optimization in edge-constrained computing environments.By summariz-ing the main optimization methods in areas such as shared storage space,activation function optimization,attention mechanism opti-mization,relative position encoding optimization,automatic mixed-precision training,and quantization techniques.It analyzes the practical results of large model optimization on the ROCm open-source framework and HIP programming model,and their impact on model performance.By summarizing the characteristics of these technologies and exploring them within the open-source frame-work,it provides new insights for significantly improving computational and storage efficiency,as well as the deployment of domes-tic applications,in edge computing environments.
Keywords：large language modelsedge computingconstrained conditionsdeployment and optimizationopen-source framework
Publication Date：2025-11-20
Online Publishing Date：2026-01-28(First online date of this platform, not the publication date of the document)
Pages：5( 3198-3201,3263 )