NVIDIA has published an article explaining the role of tokens in AI, describing them as the fundamental units of data that AI models process during training and inference. These tokens help AI systems understand relationships between words, images, and other data types, enabling capabilities such as prediction, generation, and reasoning.

The article discusses how AI factories—data centers optimized for AI workloads—process tokens efficiently, turning them into intelligence. With advances in hardware and software, companies have been able to reduce the computational cost per token, significantly increasing efficiency and revenue generation.

Tokenization is a key step in AI processing, where data is broken into smaller, meaningful components. For large language models, words are often split into tokens, with different meanings assigned distinct numerical values. In the case of AI models handling images, audio, or video, tokens represent visual or acoustic elements that enable machines to process and interpret sensory data.

During AI training, models process billions or even trillions of tokens to improve accuracy. Inference—the process of generating responses—relies on tokens as well, with models using learned patterns to produce meaningful outputs. The size of an AI model's context window determines how many tokens it can process at once, influencing its ability to generate detailed responses or analyze large datasets.

NVIDIA highlights that token-based AI economics is emerging as a key factor in AI development. AI factories measure efficiency based on token consumption, offering pricing models that reflect the number of tokens used for input and output. The trade-offs between response speed and token processing determine the user experience in AI applications, particularly for chatbots, video generation, and reasoning-based AI systems.

The company provides a full-stack AI platform designed to optimize token usage across various industries, helping developers and enterprises maximize the value of their AI applications. For further details, NVIDIA offers resources at build.nvidia.com.