Inference
The process of running a trained AI model to generate predictions or outputs — as opposed to training, which is the process of building the model. Inference accounts for the majority of AI's ongoing energy consumption.
While training a frontier model is a one-time (if massive) compute cost, inference runs continuously as millions of users interact with AI systems daily. A single ChatGPT query uses roughly 10x the electricity of a Google search. As AI adoption scales to billions of users, inference energy demand is projected to far exceed training costs, contributing to the projected growth of AI electricity consumption from 560 TWh in 2025 to potentially 1,000 TWh by 2030.
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Related Terms
AI Compute
The computational resources — primarily GPU and TPU processing power — required to train and run AI models, typically measured in FLOP (floating-point operations) or GPU-hours.
Capex (Capital Expenditure)
Long-term investment spending by companies on physical assets like data centers, GPU clusters, and networking infrastructure — the backbone of AI deployment at scale.
ChatGPT
OpenAI's conversational AI assistant, launched in November 2022, which catalyzed the current generative AI boom by demonstrating the capabilities of large language models to a mainstream audience.
Data Center
A facility housing computer systems and infrastructure used to process, store, and distribute data — increasingly built specifically for AI training and inference workloads.
Fine-Tuning
The process of further training a pre-trained AI model on a specific, smaller dataset to specialize it for a particular task or domain, requiring far less compute than training from scratch.
Foundation Model
A large AI model trained on broad data that can be adapted to a wide range of downstream tasks — examples include GPT-4, Claude, Gemini, and Llama.
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