Short answer
Inference happens when an already-trained AI model processes a prompt, image, audio file or other input to generate a result. Every ChatGPT response, AI image generation or recommendation request requires inference compute.
Inference is AI model execution
During inference, a trained model analyzes incoming data and produces predictions or generated content. Unlike training, inference does not teach the model new knowledge. Instead, it uses previously learned parameters to respond to users in real time.
Training and inference are different
Training builds the model by processing massive datasets over long periods using huge amounts of compute. Inference is the operational phase where users interact with the trained model. Training is usually more compute-intensive per event, but inference happens continuously at global scale.
Inference requires GPUs and specialized hardware
Modern AI inference often runs on GPUs or AI accelerators optimized for parallel processing. Large language models can require significant memory bandwidth and compute power, especially when serving millions of users simultaneously.
Inference consumes electricity
Every inference request consumes electricity through compute hardware, networking, storage and cooling infrastructure. As AI adoption grows worldwide, inference workloads are becoming an increasingly important part of global data center electricity demand.
Inference can be optimized
AI providers continuously optimize inference through batching, quantization, model distillation, caching and more efficient hardware. These techniques aim to reduce latency, electricity consumption and operational costs while maintaining model quality.