What Are AI Tokens? How They Work and Impact GPU Performance

An AI token is a unit of data, often a piece of text that a language model converts into numerical representations, processes using a neural network, and uses to generate output one token at a time.

Authors describe tokens as the language or currency of AI models. These are the backbone of AI systems. AI models process tokens to learn relationships between data and enable capabilities such as content generation, reasoning, or inference.

When an AI model processes text, images, audio clips, videos, or another modality, it translates the data into tokens. This process is known as tokenization. Let’s understand how it works.

What is AI Tokenization

Imagine you're talking to an AI model such as a chatbot, and you type:

“How does AI work?”

A computer doesn't naturally understand this sentence the way a human does. Before the AI model processes it, the text is converted into numbers called tokens.

What Are Tokens in AI Models

In this way, a token is a unit of text, and a token ID is the number assigned to that unit. The model works with numerical representations, not raw words directly.

Just as a model converts incoming text into tokens for processing, it generates output tokens that are converted back into readable text for the user.

AI models process different types of data by converting them into numerical representations or tokens. Similarly, image models break images into patches or visual tokens that capture patterns, shapes, and features. Audio models convert sound into representations such as acoustic tokens or spectrograms to analyze speech, sounds, and meaning. Video models process sequences of frames, often combining visual tokens with temporal information to understand movement and events over time. These representations enable AI models to analyze, interpret, and generate content across different modalities.

What do tokens have to do with GPUs?

A model’s efficiency depends partly on how quickly it processes tokens. Faster token processing improves inference speed and responsiveness. However, overall performance also depends on the model architecture, computational hardware, and memory bandwidth.

This connects tokens to AI hardware. High-performance accelerators such as NVIDIA H100 GPUs help accelerate AI inference workloads by processing the computations required to generate tokens.

When an AI model runs, GPUs perform enormous numbers of mathematical operations to process the numerical representations of tokens.

There are three related concepts:

• Tokens: Units of text the model processes and generates.
• Model parameters: Learned numerical values inside the neural network that help it recognize patterns and produce outputs.
• GPU memory (VRAM): Stores model parameters, intermediate computations, and other data needed during inference.

When you send a long prompt, the model generally has more input tokens to process. When it generates a long answer, it generates more output tokens. Both affect computation, memory use, and often the cost of running the model.

What is a context window for AI Tokens?

When you send a prompt to an AI model, your text is converted into tokens. The model processes these tokens along with relevant conversation history and other context to generate a response.

The context window is the maximum number of tokens the model can accommodate in a single processing context. It includes input tokens and, depending on the model, the tokens generated in its response.

For example, if a model has a context window of 128,000 tokens, it can accommodate up to that limit across the prompt, conversation history, and output, subject to its specific rules.

What is an AI Inference Token?

Inference is when a trained AI model uses what it has learned to answer a question or perform a task.

For example, you ask, “What is cybersecurity?”

1. Your question is converted into input tokens.
2. The AI model processes those tokens.
3. It generates output tokens that form the answer.

That's inference. The model is using its existing training rather than training itself again.

How are Tokens used in AI reasoning?

AI reasoning is when a model works through a problem in multiple steps before producing its final answer.

For example, you ask, “Why has a company's website traffic dropped, and how can it recover?”

A reasoning model might:

• Examine the available traffic data.
• Consider possible causes.
• Compare evidence and evaluate explanations.
• Develop recommendations.
• Generate its final answer.

Reasoning adds intermediate problem-solving steps that can consume more tokens, computation, and time. It affects tokens and the context window.

Therefore:

• Token consumption increases because the model processes and generates more tokens.
• Context window usage increases when more input, conversation history, or reasoning tokens are retained in the active context.
• Computational demand increases because the model must perform more calculations.

How tokens flow through an AI agent

An AI agent goes a step further. It uses an AI model to work toward a goal by planning tasks, making decisions, using tools, and taking multiple actions with limited human intervention. Each step consumes input and output tokens. The more steps an agent takes and the more information it handles, the more tokens it may consume. To understand how tokens work in agentic AI, think of an AI agent as a system that doesn't just answer a question once. It can plan, use tools, examine results, and take further actions until it completes a task.

The token system works throughout this entire process. Every time the agent processes information or generates text, it may consume tokens.

Imagine you ask an AI agent: “Research the top cybersecurity trends and prepare a report.”

How Tokens are Used by an AI Agent

The important part is that the process can repeat. The agent doesn't necessarily consume tokens only once per task.

Different AI models and use cases may have different context windows.

Where exactly are tokens consumed?

There are four main areas:

Input tokens

Instructions, conversation history, system prompts, and information returned by tools that the model receives.

Reasoning and planning

Some models generate internal reasoning tokens or other intermediate outputs while deciding what to do next. The details depend on the model.

Tool-use cycles

Search queries, tool-call arguments, retrieved results, and subsequent decisions can add to token usage when sent to the model.

Output tokens

The final report, summary, code, or other content generated by the agent.

Why Is Demand for Tokens Surging with the Arrival of Agentic AI ?

Organizations are increasingly investing in building their own AI factories to develop, train, and run AI models within their infrastructure. As businesses adopt generative AI, reasoning models, and agentic AI systems, the demand for tokens continues to grow. This shift is driving greater demand for computing power and efficient AI infrastructure capable of processing tokens faster and at a lower cost. GPUs such as NVIDIA H200 GPUs, with their high-bandwidth memory capabilities, are designed to support demanding AI workloads, including large-scale inference and long-context processing.

As NVIDIA CEO Jensen Huang mentioned at the GTC conference 2026 in Taiwan

“Tokens are now in incredible demand. Tokens are now profitable units of revenue. Businesses now demand more tokens and build more AI factories, which is the reason why computer demand here in Taiwan has skyrocketed.”

The Goal: Lower AI Infrastructure Costs

Incurring token costs is an important consideration when using advanced AI models. The goal is to complete a task accurately with the lowest practical computational cost and latency.

Important metrics include:

• Tokens per second: How quickly a model generates tokens.
• Time to first token: How long it takes to begin generating a response.
• Input and output token costs: The cost of processing prompts and generating responses.
• Tokens per task: The total token consumption required to complete a workflow.
• Task success rate: Whether the agent actually completes the task correctly.

To address the growing demand for AI tokens, NVIDIA has introduced advanced GPU architectures, including Blackwell, Blackwell Ultra, and Vera Rubin, designed to improve AI inference performance while reducing the cost per token. For example, NVIDIA B200 GPUs use the Blackwell architecture to support demanding generative AI and large-scale inference workloads, helping organizations build more capable and efficient AI infrastructure.

Comparing NVIDIA's token-efficiency improvements

Final Takeaway

AI tokens are becoming a key unit of AI computing demand. As organizations move beyond simple chatbots to reasoning models and autonomous AI agents, the amount of information processed and generated continues to grow. This is pushing demand for computing power, GPU capacity, and AI infrastructure. The real opportunity lies not just in processing more tokens, but in processing them faster, more efficiently, and at a lower cost. As AI adoption scales, the ability to deliver more useful output per token could become a significant competitive advantage.