The rising number of connected devices and data demands are putting a significant strain on network infrastructure. AI-native networks, especially those using agentic AI, are addressing these challenges by implementing fundamental architectural changes. Companies like SoftBank and Tech Mahindra have developed new LTMs and AI agents, while Amdocs, BubbleRAN, and ServiceNow are enhancing their network operations and optimization with new AI agents, all by utilizing NVIDIA AI Enterprise – https://blogs.nvidia.com/blog/telecom-agentic-ai-for-network-operations/
(Image Credit – Nvidia blog)
The Scale of Modern Telecommunications
Modern telecom networks manage an unprecedented scale of operations, handling millions of daily user connections and processing over 3,800 terabytes of data per minute. This massive data flow comes from diverse sources including base stations, routers, switches, and data centers, creating a complex ecosystem of unstructured information that traditional automation tools struggle to manage effectively.
At the GTC global AI conference last week (week of March 18, 2025), NVIDIA announced a significant advancement in telecommunications AI: industry partners are developing specialized Large Telco Models (LTMs) and AI agents specifically for telecommunications. These solutions are built using NVIDIA NIM and NeMo microservices within the NVIDIA AI Enterprise software platform, representing a new generation of AI-powered network operations.
Large Telco Models (LTMs): Revolutionizing Telecommunications with AI
Large Telco Models (LTMs) represent a significant advancement in the telecommunications industry, leveraging AI to interpret and manage complex network data. These models, analogous to language models in their ability to understand and process information, are specifically designed to analyze the intricacies of telecommunication networks. By interpreting signals, events, and metrics, LTMs offer a range of capabilities that enhance network performance, reliability, and adaptability.
Technological Foundation:
LTMs are built on a foundation of advanced AI technologies, including machine learning and deep learning algorithms. These algorithms enable the models to process vast amounts of data and identify complex patterns that would be difficult or impossible for humans to detect. Additionally, LTMs often incorporate specialized microservices that are tailored to specific telecommunication tasks, ensuring optimal performance and low latency.
LTMs represent a significant step forward in the evolution of telecommunication networks. By harnessing the power of AI, these models offer a range of benefits, including improved network performance, enhanced reliability, and increased adaptability. As LTMs continue to develop and mature, they are likely to play an increasingly important role in shaping the future of telecommunications, paving the way for more intelligent, efficient, and responsive networks.
Large Language Models (LLMs) in Network Operations
Enhanced Network Reliability and Performance through AI
Large Language Models (LLMs), when integrated into network operations, can significantly enhance the reliability, performance, and security of telecommunication networks. These AI models offer advanced capabilities for predicting and preventing network failures, optimizing network performance, and proactively addressing security threats.
1. Minimizing Network Downtime
LLMs can analyze vast amounts of network data, including historical performance data, real-time network traffic, and equipment logs, to identify patterns and anomalies that may indicate potential network failures. By predicting these failures before they occur, network operators can take proactive measures to prevent them, such as rerouting traffic, adjusting network configurations, or performing preventive maintenance. This can significantly reduce network downtime, ensuring that critical communication services remain available to users.
2. Improving Service Quality
LLMs can also be used to optimize network performance and improve service quality. By analyzing network traffic patterns and identifying areas of congestion or bottlenecks, these models can recommend adjustments to network configurations or resource allocation to ensure that traffic is routed efficiently and that network resources are utilized effectively. Additionally, LLMs can assist in faster problem resolution by automating the diagnosis and troubleshooting of network issues, reducing the time it takes to restore service and minimizing the impact on users.
3. Enhancing Network Security
Network security is a critical concern for telecommunications companies, and LLMs can play a valuable role in enhancing network security. These models can continuously monitor network traffic for signs of malicious activity, such as intrusion attempts, denial-of-service attacks, or data breaches. By identifying and responding to these threats in real time, LLMs can help prevent security breaches and protect sensitive user data. Additionally, LLMs can assist in identifying and patching network vulnerabilities, reducing the risk of exploitation by attackers.
Advancements in Network Management Technology
The integration of LLMs into network operations represents a significant advancement in network management technology. These AI models provide telecommunications companies with new tools for data-driven and automated management of their network infrastructure. By leveraging the power of LLMs, network operators can move from a reactive approach to network management to a more proactive and predictive approach, enabling them to anticipate and address network issues before they impact users.
Industry Adoption
This AI shift, as highlighted at GTC 2025, is being led by major telecommunications companies who are actively implementing these advanced technologies to optimize their networks and improve efficiency.
SoftBank and Tech Mahindra, for instance, are at the forefront of this transformation, developing new LLMs and AI agents that can automate and enhance various aspects of network management. Similarly, Amdocs, BubbleRAN, and ServiceNow are deploying AI agents to streamline network operations, enabling faster response times, predictive maintenance, and intelligent resource allocation.
This trend is not limited to a few early adopters. A recent survey indicates that a significant portion, approximately 40%, of telecommunications companies are already in the process of integrating AI into their network planning and operations. This widespread adoption underscores the growing recognition of AI’s potential to revolutionize the telecommunications sector.
Conclusion
The move towards AI-driven intelligent operations represents a paradigm shift from traditional automation methods. By leveraging AI and LLMs, telecommunications companies can create more agile, responsive, and efficient networks that can adapt to changing demands and deliver superior performance. This transformation is not only changing the way networks are managed but also redefining the future of the telecommunications industry.