Over the last year, the demand for deploying trained models in real-time applications has surged, along with a continuous stream of challengers and disruptors entering the scene. AI inference, the process of using a trained AI model to make predictions or decisions by crunching new data inside neural-networked systems, has become a critical and complex growth area. It’s backed by deep investment and projected to grow at a compound annual growth rate (CAGR) of 19.2% through 2030. Right now, a top concern across the industry is simple: We need to process significantly more data (tokens) through more AI models for dramatically less cost. Model size, data movement and computational demands all stack up and every one of them impacts ROI.