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Introducing Large Language Models as the Next Challenging Internet Traffic Source

Nataliia Koneva, Alejandro Leonardo García Navarro, Alfonso Sánchez-Macián, José Alberto Hernández, Moshe Zukerman, Óscar González de Dios

TL;DR

The paper investigates whether Generative AI and Large Language Models will become a major new source of Internet traffic by introducing the Internet of Agents (IoA) concept. It conducts a measurement-based PoC using a two-agent setup across seven LLMs, capturing both localhost and external API traffic with Wireshark to quantify per-prompt data transfer, reporting an average of about 7.59 kB per prompt-response. The results reveal model-dependent traffic patterns, with OpenAI APIs showing greater efficiency and other models exhibiting higher variability and overhead. The authors forecast substantial traffic growth driven by IoA, estimating up to hundreds of Exabytes per month in the near future and highlighting a need for network operators to prepare for multimodal and inter-agent AI traffic as adoption accelerates.

Abstract

This article explores the growing impact of large language models (LLMs) and Generative AI (GenAI) tools on Internet traffic, focusing on their role as a new and significant source of network load. As these AI tools continue to gain importance in applications ranging from virtual assistants to content generation, the volume of traffic they generate is expected to increase massively. These models use the Internet as the global infrastructure for delivering multimedia messages (text, voice, images, video, etc.) to users, by interconnecting users and devices with AI agents typically deployed in the cloud. We believe this represents a new paradigm that will lead to a considerable increase in network traffic, and network operators must be prepared to address the resulting demands. To support this claim, we provide a proof-of-concept and source code for measuring traffic in remote user-agent interactions, estimating the traffic generated per prompt for some of the most popular open-source LLMs in 2025. The average size of each prompt query and response is 7,593 bytes, with a standard deviation of 369 bytes. These numbers are comparable with email and web browsing traffic. However, we envision AI as the next "killer application" that will saturate networks with traffic, such as Peer-to-Peer traffic and Video-on-demand dominated in previous decades.

Introducing Large Language Models as the Next Challenging Internet Traffic Source

TL;DR

The paper investigates whether Generative AI and Large Language Models will become a major new source of Internet traffic by introducing the Internet of Agents (IoA) concept. It conducts a measurement-based PoC using a two-agent setup across seven LLMs, capturing both localhost and external API traffic with Wireshark to quantify per-prompt data transfer, reporting an average of about 7.59 kB per prompt-response. The results reveal model-dependent traffic patterns, with OpenAI APIs showing greater efficiency and other models exhibiting higher variability and overhead. The authors forecast substantial traffic growth driven by IoA, estimating up to hundreds of Exabytes per month in the near future and highlighting a need for network operators to prepare for multimodal and inter-agent AI traffic as adoption accelerates.

Abstract

This article explores the growing impact of large language models (LLMs) and Generative AI (GenAI) tools on Internet traffic, focusing on their role as a new and significant source of network load. As these AI tools continue to gain importance in applications ranging from virtual assistants to content generation, the volume of traffic they generate is expected to increase massively. These models use the Internet as the global infrastructure for delivering multimedia messages (text, voice, images, video, etc.) to users, by interconnecting users and devices with AI agents typically deployed in the cloud. We believe this represents a new paradigm that will lead to a considerable increase in network traffic, and network operators must be prepared to address the resulting demands. To support this claim, we provide a proof-of-concept and source code for measuring traffic in remote user-agent interactions, estimating the traffic generated per prompt for some of the most popular open-source LLMs in 2025. The average size of each prompt query and response is 7,593 bytes, with a standard deviation of 369 bytes. These numbers are comparable with email and web browsing traffic. However, we envision AI as the next "killer application" that will saturate networks with traffic, such as Peer-to-Peer traffic and Video-on-demand dominated in previous decades.

Paper Structure

This paper contains 9 sections, 6 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Generative AI and Large Language Models
  3. Internet of Agents: Use cases and applications
  4. Experiments
  5. Proof of Concept and Scenario
  6. Traffic Measurement Methodology and Source-Code
  7. Results
  8. IoA traffic forecasts
  9. Summary and discussion

Figures (6)

  • Figure 1: Use-cases and applications of AI Agents in future life
  • Figure 2: Experimental setup and packet capture points
  • Figure 3: (a) LLM traffic workflow showing localhost and Wi-Fi packet exchanges
  • Figure 4: (b) Localhost traffic capture from Wireshark between Querying and responding agents
  • Figure 5: (c) External network traffic capture from Wireshark between Responding Agent and LLM APIs
  • ...and 1 more figures