Beginner eyes local AI for 1,500-article project

Scaling a 1,500-article project locally is a bold move that trades recurring subscription costs for significant upfront hardware investment and technical overhead. Deep research remains the primary bottleneck for local models; smaller LLMs still lag behind frontier models like Claude 3.5 in factual accuracy, making RAG or agentic pipelines mandatory for this scale. Furthermore, the high hardware requirements and the "technical tax" of maintenance often outweigh the $20 monthly savings for non-technical users. While local workflows are ideal for formatting and brand voice consistency, a hybrid local-cloud approach remains the most pragmatic path for high-stakes, large-scale production.