What is Inference?
In artificial intelligence, inference is the process of running live data through a trained AI model to make a prediction or generate an output. If "training" is the phase where the AI learns, "inference" is the phase where the AI actually goes to work.
What is a simple example?
Training is like studying for a test. You read books, take practice exams, and memorize the material. Inference is taking the actual test, where you apply what you learned to answer new questions. When you type a question into a chatbot and wait for the text to stream onto your screen, you are watching inference happen in real-time.
What is it commonly confused with?
Inference is the direct counterpart to Model Training. Training adjusts the parameters; inference uses the frozen parameters to compute a result.
Why does it matter?
Inference requires significant computational power. When you hear about tech companies buying hundreds of thousands of GPUs, they need them both for training massive new models and for running inference to serve the millions of daily users interacting with their deployed AI applications.