From NVIDIA: 2025-04-23 11:00:00

AI models require a delicate balance for maximum value as inference, the process of running data through a model, incurs costs for every token generated. Inference costs have been decreasing, with a 280-fold drop for GPT-3.5 level systems. Enterprises must scale accelerated computing to maintain efficient, cost-effective AI solutions. Key terms like tokens, throughput, latency, and energy efficiency are crucial for understanding the economics of inference. The three AI scaling laws – pretraining, post-training, and test-time scaling – are essential to grasp. Profitable AI necessitates a full-stack approach with advanced hardware and software optimization. NVIDIA’s AI factory product roadmap aims to meet computational demand and complexity while maximizing efficiency for AI inference.



Read more at NVIDIA: How the Economics of Inference Can Maximize AI Value