Published On: January 10, 2026

Nano Banana The Changing Shape Of AI Workloads AI workloads have rapidly evolved. They have shifted from lengthy, compute-intensive training runs to an ongoing cycle. This cycle includes training, deployment, inference, and refinement. AI systems today are expected to respond in real time, operate at scale, and run reliably across a wide range of environments. This shift has quietly but fundamentally changed what AI demands from computing hardware. In practice, much of the growth in AI compute now comes from inference rather than training. Models are trained in centralized environments and then deployed broadly. They support recommendations, image analysis, speech translation, and generative applications. These inference workloads run continuously. They often operate under tight latency and cost constraints. They favor efficiency and predictability over peak performance. As […]