AI Inference
Using a trained AI model to make predictions or generate outputs.
Definition
AI Inference is the process of using a trained machine learning model to make predictions, generate outputs, or process new data. Unlike training which teaches the model patterns from data, inference applies that learned knowledge to new inputs.
Inference can happen on servers (cloud inference), on devices (edge inference), or hybrid approaches. Inference speed and cost depend on model size, hardware, and optimization techniques like quantization.
Why It Matters
Inference costs often exceed training costs for production AI systems, making optimization critical for profitability. Faster inference enables real-time applications like chatbots and voice assistants.
Businesses must balance inference quality, speed, and cost. Techniques like model distillation, quantization, and caching reduce inference costs while maintaining acceptable performance.
Examples in Practice
When you ask ChatGPT a question, inference generates the response using the pre-trained model. Image recognition apps run inference on your device to classify photos without sending them to the cloud.
Recommendation systems run billions of inferences daily to suggest products, content, or connections based on user behavior and preferences.