As enterprises increase adoption of advanced Artificial Intelligence models, they must navigate the distinct challenge of inference—the process of running data through a model to obtain outputs—which entails ongoing computational expenses unlike the one-time costs of model pretraining. Inference requires generating tokens in response to every prompt, and as model use scales, so do these operational costs. The recent trend has seen inference costs drop significantly due to model optimization, improved accelerated computing infrastructure, and efficient full-stack solutions, making scalable Artificial Intelligence more attainable for organizations of all sizes.
Key terminology is vital to understanding the economics of inference. Tokens, the smallest unit of data in a model, form the basis of throughput (tokens processed per second) and latency (the wait time for an output). Two crucial latency benchmarks are ´time to first token´ and ´time per output token.´ However, focusing solely on these metrics can be misleading, so organizations increasingly track ´goodput,´ which balances throughput, latency, and operational costs to maintain desired user experience and efficiency. Energy efficiency, measured as computational performance per watt, is also a growing focus as organizations seek to maximize output while minimizing energy consumption through accelerated hardware.
The economics of inference are further shaped by scaling laws. Pretraining scaling increases intelligence through enhanced data and compute, while post-training techniques like fine-tuning boost specificity. Test-time scaling, or intensive reasoning, allows models to evaluate more options for better answers but at higher computational expense. Enterprise models that employ these advanced techniques deliver higher-value, more accurate outputs, but require robust and optimized infrastructure to keep costs manageable. Modern approaches—exemplified by NVIDIA´s AI factory concept—integrate advanced hardware, networking, and software to deliver flexible, high-performance inference environments. These ´AI factories´ utilize inference management systems to maximize throughput and control expenses, supporting next-generation Artificial Intelligence applications without unsustainable cost increases.
