Generative AI for Network Operations Centers

Plan, build, and operate telecommunications networks securely with generative AI.


Generative AI



Business Goal



NVIDIA AI Enterprise

Data Intensity Is Challenging Network Uptime for Telcos

Telecommunication operators face increasing demand from data-intensive workloads that consume significant bandwidth and strain their network infrastructure. These include  services such as high-definition video streaming, digital gaming, enterprise video conferencing, and social media. In addition, 5G deployments require substantial network upgrades to meet the demand for ultra-fast, low-latency connectivity. The shift to cloud-based services requires reliable, high-bandwidth connections, which causes additional stress. And the proliferation of mobile devices adds to network congestion from data, voice calls, and messaging.

Building and maintaining network infrastructure to support the increasing demand for data and connectivity with a high quality of service requires substantial investment from telcos. Telcos must troubleshoot and repair equipment during outages, test network backups, ensure security, and update technical documentation.  Meeting expectations for fast, reliable, and affordable services and providing excellent customer service and support are constant challenges.

Generative AI Enhances Network Operations and Planning

AI is reshaping the way telecommunication providers operate their networks, including network modeling and simulation, cybersecurity, and logistics and routing optimization. Generative AI lets telcos interact with their networks in new, powerful ways to proactively monitor performance and predict potential degradations and customer impact. This includes responding to queries on critical issues, identifying incidents within specified time frames, and recommending solutions. When field technicians are called out to the physical network, dynamic route optimization and virtual field agent assistants improve the speed and accuracy of service, enhancing customer satisfaction while reducing travel time and fuel costs.

Generative AI applications can guide network planning. Virtual assistants support field technicians by answering specific questions around network planning to find unused resources and capacity. In addition, generative AI can provide insights into network failures and performance degradations with prioritization of network fallout to limit impacts.

Boosting Network Performance and Efficiency With Accelerated Computing

Global telecommunications companies are exploring how to cost-effectively deliver new AI applications to the edge over 5G and upcoming 6G networks. With NVIDIA accelerated computing and AI, telcos, cloud service providers (CSPs), and enterprises can build high-performance cloud-native networks—both fixed and wireless—with improved energy efficiency and security. 

The NVIDIA AI Foundry for Generative AI

The NVIDIA AI foundry—which includes NVIDIA AI Foundation models, the NVIDIA NeMo™ framework and tools, and NVIDIA DGX™ Cloud—gives enterprises an end-to-end solution for developing custom generative AI. 

Amdocs, a leading software and services provider, plans to build custom large language models (LLMs) for the $1.7 trillion global telecommunications industry using the NVIDIA AI foundry service on Microsoft Azure. In network operations, Amdocs and NVIDIA are exploring ways to generate solutions that address configuration, coverage, and performance issues as they arise, including:  

  • Building a generative AI assistant to answer questions around network planning
  • Providing insights and prioritization for network outages and performance degradations
  • Optimizing operations by using generative AI to monitor, predict, and resolve network issues, manage resources in real time​, monitor network diagnostics, analyze service and user impact, prioritize impact-based recommendations, and execute orchestration activation


ServiceNow is integrating generative AI capabilities into their Now Platform and enriching all workflows with Now Assist, their generative AI assistant. ServiceNow leverages NeMo and NVIDIA Triton™ Inference Server (both part of NVIDIA AI Enterprise), NVIDIA AI Foundation models, and DGX systems to build, customize, and deploy generative AI models for telecom customers. These include use cases in network operations:

  • Automated service assurance: Distill and act on volumes of complex technical data generated from network incidents​ and summarized by generative AI.
  • Streamlined service delivery​: Dynamically create order tasks with generative AI to reduce human errors, ensure accurate service delivery, and improve customer satisfaction and loyalty.
  • Optimized network design: Manage diverse network services, local configurations, and rulings to improve network design.


NeMo provides an end-to-end solution—including enterprise-grade support, security, and stability—across the LLM pipeline, from data processing to training to inference of generative AI models. It allows telcos to quickly train, customize, and deploy LLMs at scale, reducing time to solution while increasing return on investment.

The NVIDIA AI foundry includes NVIDIA AI Foundation models, the NeMo framework and tools, and NVIDIA DGX™ Cloud , giving enterprises an end-to-end solution for creating custom generative AI models.

Once generative AI models are built, fine-tuned, and trained, NeMo enables seamless deployment through optimized inference on virtually any data center or cloud. NeMo Retriever, a collection of generative AI microservices, provides world-class information retrieval with the lowest latency, highest throughput, and maximum data privacy, enabling organizations to generate insights in real time. NeMo Retriever enhances generative AI applications with enterprise-grade retrieval-augmented generation (RAG), which can be connected to business data wherever it resides.

NVIDIA DGX Cloud is an AI-training-as-a-service platform, offering a serverless experience for enterprise developers that’s optimized for generative AI. Enterprises can experience performance-optimized, enterprise-grade NVIDIA AI Foundation models directly from a browser and customize them using proprietary data with NeMo on DGX Cloud.

NVIDIA AI Enterprise for Accelerated Data Science and Logistics Optimization

The NVIDIA AI Enterprise software suite enables quicker time to results for AI and machine learning initiatives, while improving cost-effectiveness. Using analytics and machine learning, telecom operators can maximize the number of completed jobs per field technician​, dispatch the right personnel for each job, dynamically optimize routing based on real-time weather conditions​, scale to thousands of locations​, and save billions of dollars in maintenance.

AT&T is transforming their operations and enhancing sustainability by using NVIDIA-powered AI for processing data, optimizing fleet routing, and building digital avatars for employee support and training. AT&T first adopted the NVIDIA RAPIDS™ Accelerator for Apache Spark to capitalize on energy-efficient GPUs across their AI and data science pipelines. Of the data and AI pipelines targeted with Spark RAPIDS, AT&T saves about half of their cloud computing spend and sees faster performance, while reducing their carbon footprint.

AT&T, which operates one of the largest field dispatch teams, is currently testing NVIDIA® cuOpt™ software to to handle more complex technician routing and optimization challenges. In early trials, cuOpt delivered solutions in 10 seconds, while the same computation on x86 CPUs took 1,000 seconds. The results yielded a 90 percent reduction in cloud costs and allowed technicians to complete more service calls each day.

Quantiphi, an innovative AI-first digital engineering company, is working with leading telcos to build custom LLMs to support field technicians​. Through LLM-powered virtual assistants acting as copilots, Quantiphi is helping field technicians resolve network-related issues and manage service tickets raised by end customers.

“Ask AT&T was originally built on OpenAI’s ChatGPT functionality. But Ask AT&T is also interoperable with other LLMs, including Meta’s LLaMA 2 and the open-source Falcon transformers. We’re working closely with NVIDIA to build and customize LLMs. Different LLMs are suited for different applications and have different cost structures, and we’re building that flexibility and efficiency in from the ground floor.”

Andy Markus, Chief Data Officer, AT&T

Getting Started With Generative AI for Network Operations

Telecommunications operators looking to build custom generative AI models for enterprise applications can employ the NVIDIA AI foundry, which has four distinct steps:

  1. Start with state-of-the-art generative AI models: Leading foundation models include Llama 2, Stable Diffusion, and NVIDIA’s Nemotron-3 8B family, optimized for the highest performance per cost.

  2. Customize foundation models: Tune and test the models with proprietary data using NVIDIA NeMo.

  3. Build models faster in your own AI factory: Streamline AI development on NVIDIA DGX Cloud.

  4. Deploy and scale: Run generative AI applications anywhere—cloud, data center, or edge—by deploying with NVIDIA AI Enterprise.

NeMo is an end-to-end, cloud-native framework for building, customizing, and deploying generative AI models anywhere. It includes training and inferencing frameworks, guardrailing toolkits, data curation tools, and pretrained models, offering enterprises an easy, cost-effective, and fast way to adopt generative AI.

As generative AI models rapidly evolve and expand, the complexity of the AI stack and its dependencies grows. For enterprises running their business on AI, NVIDIA AI Enterprise provides a production-grade, secure, end-to-end software platform, which includes NeMo, as well as generative AI reference applications and enterprise support to streamline adoption.

NVIDIA DGX Cloud provides developers with easy-to-use, serverless platform to streamline and accelerate the development and deployment of generative AI applications, multi-node training capabilities, and near-limitless scalability of GPU resources.


NVIDIA NIM, part of NVIDIA AI Enterprise, is an easy-to-use runtime designed to accelerate the deployment of generative AI across your enterprise. This versatile microservice supports open community models and NVIDIA AI Foundation models from the NVIDIA API catalog, as well as custom AI models. NIM builds on NVIDIA Triton™ Inference Server, a powerful and scalable open source platform for deploying AI models, and is optimized for large language model (LLM) inference on NVIDIA GPUs with NVIDIA TensorRT-LLM. NIM is engineered to facilitate seamless AI inferencing with the highest throughput and lowest latency, while preserving the accuracy of predictions. You can now deploy AI applications anywhere with confidence, whether on-premises or in the cloud.

NVIDIA NeMo Retriever

NeMo Retriever is a collection of CUDA-X microservices enabling semantic search of enterprise data to deliver highly accurate responses using retrieval augmentation. Developers can use these GPU-accelerated microservices for specific tasks including ingesting, encoding, and storage large volumes of data, interacting with existing relational databases, and searching for relevant pieces of information to answer business questions.

Generative AI can analyze large volumes of data from equipment sensors to predict potential failures or issues. This helps technicians anticipate problems before they occur, allowing for timely maintenance and minimizing downtime.

Generative AI-driven analytics provide technicians with actionable insights and recommendations based on real-time data. This allows them to make informed decisions regarding repairs, upgrades, and network optimization.

Generative AI can automate repetitive and routine tasks, such as generating work orders, scheduling appointments, and creating documentation. This allows technicians to focus more on complex issues and customer service.

Optimize Network Operations With Generative AI

By leveraging NVIDIA AI, telecommunications companies can reduce network downtime, increase field technician productivity, and deliver better quality of service to customers. Get started by reaching out to our team of experts or exploring additional resources.


Generative AI in Practice: Examples of Successful Enterprise Deployments

Learn how telcos built mission-critical LLMs, powered by NVIDIA DGX systems and the NeMo framework, to simplify their business, increase customer satisfaction, and achieve the fastest and highest return.

Part 1: A Beginner's Guide to Large Language Models

Get an introduction to LLMs and how enterprises can benefit from them.

Part 2: How LLMs Are Unlocking New Opportunities for Enterprises

Explore how traditional natural language processing tasks are performed by LLMs, including content generation, summarization, translation, classification, and chatbot support.