现今的发布，NVIDIA Minerva是一个AIOps平台，为帮助团队更早发现问题并保持AI工厂的生产而建造。 它把基础设施和AI工作量的遥测结合起来，然后采用专门设计的AI模型，将数据变成及时的、可操作的见解。
AI工厂是世界上最复杂的业务之一，大量的GPU跨高速网络工作来训练模型和服务AI应用。 在这个规模上，单一的退化成分可以减缓整个工作。 警告信号可能很容易丢失，例如GPU运行比它的同伴更热或者NVMe驱动器在负载下失去性能。
该平台侧重于AI工厂的产量，这是通过GPU时数，良好产出，集成运行时间和象征性生产产生的有用工作基础设施的量度。
提供人工智能工厂动态视图
传统的监测工具往往将数据中心分为单独的视图。 联网、计算、站点可靠性和机器学习小组都可以看到业务的不同部分。 Minerva通过NVIDIA GPU连接这些信号，联网，存储，调度和工作量。
借助这些信号，Minerva构建了一个动态知识图，从逻辑上描绘了基础设施——GPU,SuperNIC，开关和电缆——及其上运行的工作。 这种背景帮助操作者追踪一个缓慢的训练工作到影响它的组件，甚至跨越一个拥有数十万GPU的工厂。
它利用有监督的和无监督的机器学习，分析每小时数十亿个数据点。 它学习正常的行为，发现以前未见的异常现象，根据工厂的地貌预测新出现的故障和相关的事件。 它不会把每一个症状都传染给操作员，而是可以将一个事件风暴组合成一个有潜在根源的焦点事件。
Minerva带了本地AI助手，并与外部代理AI工具连接。 这个新增是一个基于直播遥测的自然语言界面。 操作员可以通过谈话调查集群健康并接受推荐的补救。 团队可以通过基础设施应用编程接口和外部系统，让一个人保持循环，手动执行修正或连接Minerva与自动化。
实现人工智能工厂最大化的成果
将工作量和基础设施问题与其代理AI能力联系起来的能力有助于简化AI工厂的运作。 Minerva没有处理警报暴风雪，而是将相关问题汇总为一个单一的可操作项目。 这使得AIOps团队可以从被动维护转向主动维护。 这一切帮助AI工厂最大限度地提升GPU的运行时间。
Minerva为关键衡量标准提供价值。
- 中断间隔时间较长：Minerva在事故发生前就发现麻烦，所以工作持续时间更长，没有发生意外。
- 更快的修复时间：当事件发生时，Minerva发现了问题，导致明显更快的解决。
- 更深入的洞察力：现代AI基础设施产生大量的数据；Minerva过滤出来，只留下少数真正重要的警报。
IREN是NVIDIA合作伙伴之一，利用Minerva优化其AI工厂。
“随着我们的人工智能工厂规模的扩大，为我们的客户提供最佳服务的关键是最大限度地提高我们的GPU的生产率。” “NVIDIA Minerva让我们的行动小组在网络、GPU和他们身上的工作有一个视野，因此我们更注重主动维护，而不是解决问题。 这种能见度加强了我们下一步建设的基础设施的运作模式。
加入IREN,Aolani,Bitdeer AI,IBM,Jio,Lambda,Quake AI和Tesla计划部署Minerva.
除了Dell Technologies,Lenovo和Supermicro还将提供Minerva作为其AI工厂业务组合的一部分。
更多关于NVIDIA Minerva的学习。
见关于软件产品信息的通知。
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Launched today, NVIDIA Minerva is an AIOps platform built to help teams find problems earlier and keep AI factories productive. It brings together telemetry from infrastructure and AI workloads, then applies purpose-built AI models to turn that data into timely, actionable insight.
AI factories are among the most complex operations in the world, with vast numbers of GPUs working across high-speed networks to train models and serve AI applications. At that scale, a single degraded component can slow an entire job. The warning signs may be easy to miss, such as a GPU running hotter than its peers or an NVMe drive losing performance under load.
The platform focuses on AI factory yield, a measure of the useful work infrastructure produces through GPU hours, goodput, cluster uptime and token production.
Delivering a Dynamic View of an AI Factory
Traditional monitoring tools often divide the data center into separate views. Networking, compute, site reliability and machine learning teams each see a different part of the operation. Minerva connects those signals across NVIDIA GPUs, networking, storage, schedulers and workloads.
With these signals, Minerva builds a dynamic knowledge graph that logically maps the infrastructure — GPUs, SuperNICs, switches and cables — and the jobs running on it. This context helps operators trace a slow training job to the component affecting it, even across a factory with hundreds of thousands of GPUs.
It analyzes billions of data points per hour using supervised and unsupervised machine learning. It learns normal behavior, detects previously unseen anomalies, predicts emerging failures and correlates related events based on the factory’s topology. Instead of sending every symptom to an operator, it can group an event storm into a focused incident with a likely root cause.
Minerva comes with a native AI assistant and connects to external agentic AI tools. This adds a natural-language interface grounded in live telemetry. Operators can investigate cluster health and receive recommended remediation through conversation. Teams can keep a person in the loop to manually implement fixes or connect Minerva to automation through infrastructure application programming interfaces and external systems.
Results That Maximize AI Factory Yield
The ability to correlate workload and infrastructure issues along with its agentic AI capabilities help simplify AI factory operations. Instead of dealing with a blizzard of alerts, Minerva aggregates related problems into a single actionable item. This allows AIOps teams to move from reactive to proactive maintenance. And all of this helps AI factories maximize GPU uptime.
Minerva delivers value for the key metrics AI factories focus on:
- Longer time between interruptions: Minerva spots trouble before it happens, so jobs run longer without incident.
- Faster time to repair: When an incident does happen, Minerva pinpoints the problem, leading to a significantly quicker resolution.
- Greater insight: Modern AI infrastructure generates a flood of data; Minerva filters it out, leaving only the handful of alerts that actually matter.
IREN is among the NVIDIA partners using Minerva to optimize its AI factories.
“As we scale our AI factories, the key to providing the best service for our customers is maximizing the productivity of our GPUs,” said Denis Skrinnikoff, chief technology officer of IREN. “NVIDIA Minerva gives our operations team one view across the network, the GPUs and the jobs running on them, so we focus more on proactive maintenance rather than troubleshooting. That visibility strengthens our operating model for the infrastructure we’re building next.”
Joining IREN, Aolani, Bitdeer AI, IBM, Jio, Lambda, Quake AI and Tesla plan to deploy Minerva.
In addition to Dell Technologies, Lenovo and Supermicro will offer Minerva as part of their AI factory operations portfolios.
Learn more about NVIDIA Minerva.
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