NVIDIA 推出代理式 AI 蓝图与电信推理模型，推动自主网络发展

C114通信网03-04

借助全新开源大型电信模型与 NVIDIA Blueprint，电信运营商能够利用自有数据训练 AI 智能体，构建自主网络。自主网络，即具备智能、可自我管理能力的电信运营系统，正从未来愿景转变为当前电信运营商的优先战略。在 NVIDIA 最新发布的《电信行业 AI 发展现状及趋势》调研报告中，网络自动化已成为投资回报率最高、最受重视的 AI 应用场景。需要强调的是，自动化与自主化并不是一个概念。自主...

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development","themeId":null,"isJumpTheme":false,"ttsUrl":null,"symbols_score_info":{"NVDS":0.6,"NVDD":0.6,"2NVD.UK":0.6,"3NVD.UK":0.6,"NVDS.UK":0.6,"NVD3.UK":0.6,"NVII":0.6,"AIYY":0.6,"NVDY":0.6,"NVDW":0.6,"NVDU":0.6,"SNVD.UK":0.6,"NVDX":0.6,"NVD":0.6,"ANV":0.6,"NVDQ":0.6,"NVDB":0.6,"NVDA":1.95,"NVIW.SI":0.6,"NVDG":0.6,"DIPS":0.6,"NVD2.UK":0.6},"content_text":"借助全新开源大型电信模型与 NVIDIA Blueprint，电信运营商能够利用自有数据训练 AI 智能体，构建自主网络。自主网络，即具备智能、可自我管理能力的电信运营系统，正从未来愿景转变为当前电信运营商的优先战略。在 NVIDIA 最新发布的《电信行业 AI 发展现状及趋势》调研报告中，网络自动化已成为投资回报率最高、最受重视的 AI 应用场景。需要强调的是，自动化与自主化并不是一个概念。自主网络不仅能够执行预定义的工作流，更需理解运营商意图、权衡各种因素，并自主决策应采取何种行动。而实现这一转变的关键，在于基于电信数据微调的推理模型和 AI 智能体。要实现真正的网络自主，必须构建一个端到端的智能体系统，其中包含的关键组件包括电信网络模型、可相互通信的 AI 智能体，以及用于验证操作效果的网络仿真工具。在巴塞罗那世界移动通信大会（MWC 2026）前夕，NVIDIA 发布了一款开放的基于 NVIDIA Nemotron 的大型电信模型（LTM）、一份构建网络运营推理智能体的综合指南，以及用于节能与网络配置的全新 NVIDIA Blueprint，该蓝图可通过多智能体编排帮助运营商迈向自主运营。此外，作为 GSMA 新推出的 Open Telco AI 倡议的一部分，NVIDIA 将通过移动通信行业的权威组织 GSMA，以开源形式发布这款全新的 LTM 模型、实施指南及代理式 AI 蓝图，供全行业使用。开放Nemotron3大型电信模型为电信领域引入推理能力要成功在电信运营中规模化部署生成式 AI 与代理式 AI，AI 模型必须能够理解电信领域的专业语言，并对复杂工作流进行逻辑推理。为此，NVIDIA 与 AdaptKey AI 合作，推出了一款全新开源的、300 亿参数的 NVIDIA Nemotron LTM，全球运营商均可借此构建自主网络。该模型基于 NVIDIA Nemotron 3 系列基础模型构建，并由 AdaptKey AI 利用包括行业标准文档和合成日志在内的开放电信数据集进行微调，专门优化以理解电信术语，并能对故障隔离、修复方案规划、变更验证等工作流进行推理。作为一款开放模型，Nemotron LTM 使电信运营商能够完整透明地了解模型的训练方法与所用数据，从而在本地网络环境中安全、快速地部署，并直接基于此构建和运行 AI 智能体。同时，运营商还可安全地结合自身网络与运营数据，针对模型的电信推理能力进行安全地调整和扩展，在不牺牲数据控制权或安全性的情况下，稳步迈向自主运营。教会AI智能体像网络工程师一样思考NVIDIA 与 Tech Mahindra 联合发布了一份开源指南，指导电信运营商如何微调特定领域专用的推理模型，并构建能够安全执行网络运营中心（NOC）工作流的智能体。该指南提出了一套教学框架，旨在让模型“像 NOC 工程师一样思考”：聚焦高影响、高频发生的故障类别；将专家的解决方案转化为逐步操作流程；再将这些流程转化为结构化的“推理轨迹”，清晰记录每一步操作、工具调用、结果与决策依据。这些轨迹成为模型学习的“思维范例”，使模型不仅知道“该做什么”，更理解“为何这一系列检查与修复步骤是安全且有效的”。借助 NVIDIA NeMo-Skills工作流，运营商可基于这些推理轨迹对模型进行微调，从而奠定构建电信专用 AI 智能体的基础——这些智能体将具备类似网络工程师的问题解决与推理能力。通过全新的意图驱动能源效率蓝图使能效最大化自主网络依赖闭环运行：通过理解网络状态的模型，基于意图采取行动的智能体，以及将仿真结果反馈回系统以验证并优化决策的机制实现运作。NVIDIA 新推出的用于意图驱动 RAN 能源效率的 NVIDIA Blueprint 将这些要素整合在一起，帮助运营商在保障服务质量的前提下，系统性降低 5G RAN 的能耗。该蓝图集成了网络测试与测量领域的领导者 VIAVI 的 TeraVM AI RAN 场景生成器 (AI RSG) 平台，用于生成合成网络数据，包括小区利用率、用户吞吐量及其他流量模式，并将其转换为简洁、可查询的格式。随后，一个能源规划智能体会基于这些合成数据推理，生成节能策略，并在 AI RSG 中进行模拟验证。这使得运营商可在闭环环境中安全地验证节能策略是否符合其意图，而无需更改现网配置或影响用户服务。全球运营商已开始部署用于网络配置的NVIDIA Blueprint用于电信网络配置的 NVIDIA Blueprint 正被全球多家运营商采用。Cassava Technologies 正利用该蓝图构建 Cassava 自主网络平台，专为优化非洲多样化的多厂商移动网络环境而设计。该平台部署了三个智能体：一个用于监控网络并推荐配置变更，一个负责应用变更并自动生成文档与治理记录，一个评估变更影响，若出现意外后果则安全回滚。NTT DATA 则借助该蓝图，为日本某一级运营商部署智能流量调控系统。当网络因中断后用户大规模重连引发流量激增时，该系统可帮助网络平稳应对。具体而言，一个 AI 智能体会实时分析全网需求，并决定在哪些小区、何时、以何种方式接纳新用户。随着网络状况趋于稳定，智能体动态调整决策，将过去依赖人工配置的流程转变为数据驱动的优化循环，从而打造更具韧性的移动网络。通过多智能体编排，演进网络配置能力为帮助电信运营商在 RAN 中设计、观测并优化复杂的代理式工作流，NVIDIA 正在与 BubbleRAN 合作，利用 NVIDIA NeMo 智能体工具包（NAT）与 BubbleRAN 代理式工具包（BAT）这两套互补的多智能体编排框架，增强用于电信网络配置的 NVIDIA Blueprint。BubbleRAN 正将 NAT 与 BAT 集成至其 Opti-Sphere 平台，以更灵活地跨容器与工作负载管理网络监控、配置与验证智能体，并将其连接至报告网络指标与流量状态的工具，从而持续提出并验证配置变更建议。Telenor Group 将成为首家采用该蓝图与 BubbleRAN 合作的电信运营商，用于提升其旗下 Telenor Maritime（集团全球海上连接服务提供商）的 5G 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