适用于网络运营中心的生成式 AI

利用生成式 AI 安全地规划、构建和运营电信网络。

工作负载

生成式 AI

行业

电信

业务目标

风险缓解

产品

NVIDIA AI Enterprise
NVIDIA NIM 微服务
NVIDIA NeMo

生成式 AI 加速网络配置、部署和运营

2024 年,电信公司的资本支出 (CapEx) 估计接近 2950 亿美元,运营支出 (OpEx) 超过 1 万亿美元,其中包括基于人工的网络规划和维护流程支出。在电信网络中,配置和优化涉及管理大量相互依赖的参数,这些参数直接影响数百万客户和最终用户的网络性能、用户体验和频谱效率。电信网络工程师需要根据一天中的时间、用户行为、移动性、干扰和服务类型不断调整这些设置。

生成式 AI 为大型电信模型 (LTM) 和 AI 智能体提供支持,可在网络运营中实现新一代 AI,支持电信公司优化运营支出 (OpEx)、高效利用资本支出 (CapEx),并发掘新的盈利机会。NVIDIA 开发了一种代理式 AI 解决方案,通过观察实时网络 KPI、做出数据驱动型决策和自动调整参数,将自主性引入这一动态环境。

与传统的基于规则的系统不同,AI 智能体可以感知、通过复杂的权衡进行推理、从反馈回路中学习,并根据需要添加人类在环反馈来适应新的条件。它还可以在多个层和多个供应商之间编排更改,从而实现负载平衡、单元间干扰协调或在负载较轻的区域节能等协调行动。这种级别的自主控制不仅提高了效率和服务质量 (QoS),还降低了运营复杂性,并缩短了在密集、高需求环境中解决问题的时间。

 

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.

Diagram of AI model training, customization, and deployment.

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

利用生成式 AI 优化网络运营

通过利用 NVIDIA AI,电信公司可以减少网络宕机时间,提高现场技术人员的工作效率,并为客户提供更好的服务质量。联系我们的专家团队或探索其他资源。

Resources

Illustration of contact center agents supported by AI for speech-to-text and text-to-speech.

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.

Illustration of LLM use cases for generation of text, images, code, summarization, and beyond.

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

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

Illustration of LLM deployment for text-based use cases.

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.

Architecture diagram of NVIDIA AI Blueprint for telco network configuration.