This Week's Term: AI Inference - the process of running a trained AI model on real-time data to generate predictions, answers, or outputs. If training is learning, inference is putting that learning into practice.
When you ask ChatGPT a question, request Claude to analyze a document, or get an AI product recommendation from an e-commerce site, you're triggering inference. The model compares your input against patterns it learned during training and generates an appropriate response.
Here's what business leaders should understand: while training an AI model might cost millions of dollars in computing power, inference costs often dwarf training over a model's lifetime. Training happens once; inference happens millions or billions of times. A chatbot might field millions of queries daily, each requiring separate inference. By some estimates, 80-90% of an AI model's total compute costs come from inference, not training.
This has significant implications for AI strategy. The "high cost of inference" drives much of the innovation in AI hardware and software optimization - specialized chips, model compression, and techniques like quantization that make models smaller and faster without sacrificing accuracy.
For leaders evaluating AI solutions, inference costs matter as much as capabilities. A powerful model that's expensive to run may be less valuable than a smaller, optimized model that delivers results quickly and affordably at scale.
To understand inference in more depth, including how the technology stack (hardware, software, middleware) affects speed and cost, watch IBM's Martin Keen explain it clearly: