AI inference is the process by which a trained large language model (LLM) applies what it has learned to new data to make predictions, decisions, or classifications. In practical terms, the process goes like this. After a model is trained, say the new GPT 5.1, we use it during the inference phase, where it analyzes data (like a new image) and produces an output (identifying what's in the image) without being explicitly programmed for each fresh image. These inference workloads bridge the gap between LLMs and AI chatbots and agents.