# NVIDIA — NVIDIA Unveils Minerva to Bring End-to-End Observability for AI Factories

- Company: NVIDIA (nvidia.com)
- Announced: 2026-10-22T15:00:02+00:00
- Category: product-launch
- Coverage: not counted
- Announcement: yes
- Group: announcements
- Source: https://blogs.nvidia.com/blog/minerva-platform-infrastructure/
- Record: https://forck.live/items/26663-nvidia-unveils-minerva-to-bring-end-to-end-observability-for-ai-factories
- Subject: AI platform

NVIDIA launched Minerva, an AIOps platform that pulls telemetry from infrastructure and AI workloads and applies purpose-built AI models to turn it into actionable insight for AI factory operators. AI factories run vast numbers of GPUs across high-speed networks, where a single degraded component can slow an entire job and warning signs such as a GPU running hotter than its peers are easy to miss. Minerva connects signals across NVIDIA GPUs, networking, storage, schedulers and workloads, building a dynamic knowledge graph that maps GPUs, SuperNICs, switches and cables alongside the jobs running on them. It analyzes billions of data points per hour using supervised and unsupervised machine learning, learning normal behavior, detecting previously unseen anomalies, predicting emerging failures and grouping an event storm into a focused incident with a likely root cause. The platform includes a native AI assistant and connects to external agentic AI tools, giving operators a natural-language interface grounded in live telemetry for investigating cluster health and receiving recommended remediation, with a person kept in the loop or automation connected through infrastructure APIs. IREN is among the partners using Minerva, and Aolani, Bitdeer AI, IBM, Jio, Lambda, Quake AI and Tesla plan to deploy it, while Dell Technologies, Lenovo and Supermicro will offer it as part of their AI factory operations portfolios.

## Evidence

Verbatim from https://blogs.nvidia.com/blog/minerva-platform-infrastructure/:

> 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.

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