The startup deploys machine learning models on 12 servers, each processing 1.8 terabytes of sensor data daily. If data volume grows by 10% per month, how many terabytes will be processed in total over the next 3 months?

Wed, 06 May 2026 05:31:16 +0000

**How Scaling Machine Learning at 10% Monthly Growth Drives Massive Data Processing Over Time** In an era where real-time insights fuel smarter decisions, one startup is quietly managing a surge in machine learning complexity that reflects broader trends in data-driven innovation. At the heart of this growth is a tightly orchestrated system: 12 powerful servers, each handling 1.8 terabytes of sensor data every day. With data volumes rising steadily by 10% each month, understanding how much this infrastructure scales over time reveals compelling patterns behind modern AI deployment. Growing data demand is no accident—it reflects the expanding role of machine learning across industries. Advanced sensor networks now generate vast streams of information used in everything from smart manufacturing to predictive maintenance. As companies adopt these technologies, server clusters like this one scale dynamically, turning incremental monthly inputs into exponential growth. With each passing month, the raw volume compounds, reshaping processing requirements in measurable ways. This steady increase isn’t just technical noise—it’s a signal of how digital infrastructure adapts to rising analytical needs. To solve: If the startup processes 1.8 terabytes per day across 12 servers and faces 10% monthly data volume growth, how many terabytes are processed total over the next three months?