{"slug": "nvidia-unveils-minerva-to-bring-end-to-end-observability-for-ai-factories", "title": "NVIDIA Unveils Minerva to Bring End-to-End Observability for AI Factories", "summary": "NVIDIA launched Minerva, an AIOps platform that unifies telemetry across GPUs, networking, storage, schedulers and workloads and applies supervised and unsupervised machine learning to analyze billions of data points per hour, aimed at maximizing AI factory yield through GPU hours, goodput, cluster uptime and token production. IREN is among the partners using Minerva, and Aolani, Bitdeer AI, IBM, Jio, Lambda, Quake AI and Tesla plan to deploy it, alongside Dell Technologies and Lenovo. IREN chief technology officer Denis Skrinnikoff said 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.", "body_md": "Launched today, [NVIDIA Minerva](https://www.nvidia.com/networking/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.\n\nAI 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.\n\nThe platform focuses on AI factory yield, a measure of the useful work infrastructure produces through GPU hours, goodput, cluster uptime and token production.\n\n## **Delivering a Dynamic View of an AI Factory**\n\nTraditional 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.\n\nWith 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.\n\nIt 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.\n\nMinerva 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.\n\n## **Results That Maximize AI Factory Yield**\n\nThe 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.\n\nMinerva delivers value for the key metrics AI factories focus on:\n\n- **Longer time between interruptions** : Minerva spots trouble before it happens, so jobs run longer without incident.\n- **Faster time to repair** : When an incident does happen, Minerva pinpoints the problem, leading to a significantly quicker resolution.\n- **Greater insight** : Modern AI infrastructure generates a flood of data; Minerva filters it out, leaving only the handful of alerts that actually matter.\n\nIREN is among the NVIDIA partners using Minerva to optimize its AI factories.\n\n“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.”\n\nJoining IREN, Aolani, [Bitdeer AI](https://www.bitdeer.ai/en/blog/nvidia-minerva/), IBM, Jio, Lambda, Quake AI and Tesla plan to deploy Minerva. \n\nIn addition to Dell Technologies, [Lenovo](https://news.lenovo.com/europes-ai-moment-gtc-berlin-26) and Supermicro will offer Minerva as part of their AI factory operations portfolios. \n\n*Learn more about* *NVIDIA Minerva**.*\n\n*See* *notice* *regarding software product information.*", "url": "https://wpnews.pro/news/nvidia-unveils-minerva-to-bring-end-to-end-observability-for-ai-factories", "canonical_source": "https://blogs.nvidia.com/blog/minerva-platform-infrastructure/", "published_at": "2026-10-22 15:00:02+00:00", "updated_at": "2026-10-22 15:10:12.773072+00:00", "lang": "en", "topics": ["artificial-intelligence", "ai-infrastructure", "ai-agents", "mlops", "ai-tools"], "entities": ["NVIDIA", "NVIDIA Minerva", "IREN", "Denis Skrinnikoff", "Aolani", "Bitdeer AI", "IBM", "Tesla"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/nvidia-unveils-minerva-to-bring-end-to-end-observability-for-ai-factories", "markdown": "https://wpnews.pro/news/nvidia-unveils-minerva-to-bring-end-to-end-observability-for-ai-factories.md", "text": "https://wpnews.pro/news/nvidia-unveils-minerva-to-bring-end-to-end-observability-for-ai-factories.txt", "jsonld": "https://wpnews.pro/news/nvidia-unveils-minerva-to-bring-end-to-end-observability-for-ai-factories.jsonld"}}