NVIDIA Launches Minerva AIOps Platform for AI Factory Observability

NVIDIA launched Minerva on October 22, 2026, describing it as an AIOps platform that gives operations teams earlier warning of infrastructure problems across AI factories. Minerva consolidates telemetry from both infrastructure and AI workloads, then applies purpose-built AI models to convert that data into actionable insight, according to the company.

AI factories rank among the most complex operations in the world, NVIDIA says, with huge fleets of GPUs training models and serving AI applications across high-speed networks. A single degraded component at that scale — a GPU running hotter than its peers or an NVMe drive losing performance under load — can slow an entire job, and such warning signs are easy to miss. Minerva targets what NVIDIA calls AI factory yield: the useful work the infrastructure produces, measured through GPU hours, goodput, cluster uptime and token production.

A Dynamic Knowledge Graph of the Factory

Traditional monitoring tools often split the data center into separate views, with networking, compute, site reliability and machine learning teams each seeing a different part of the operation. NVIDIA says Minerva connects those signals across its GPUs, networking, storage, schedulers and workloads.

From those signals, Minerva builds a dynamic knowledge graph that logically maps GPUs, SuperNICs, switches and cables along with the jobs running on them. NVIDIA says this mapping lets operators trace a slow training job back to the component affecting it, even in a factory running hundreds of thousands of GPUs.

According to NVIDIA, the platform analyzes billions of data points per hour with supervised and unsupervised machine learning, learning what normal behavior looks like, flagging anomalies it has not encountered before, predicting failures as they emerge and correlating related events using the factory’s topology. The platform can consolidate a storm of related events into one focused incident carrying a likely root cause, rather than forwarding each individual symptom to an operator.

AI Assistant and Remediation Workflow

Minerva ships with a native AI assistant and can also connect to external agentic AI tools, giving operators a natural-language interface grounded in live telemetry. Operators can investigate cluster health and receive recommended remediation through conversation. Teams may keep a person in the loop to carry out fixes manually, or connect the platform to automation through infrastructure application programming interfaces and external systems.

Bitdeer AI, which announced its own adoption of the platform on October 22, identifies the built-in assistant as Sherlock, Minerva’s platform-native multi-agent AI assistant, and says an MCP server allows operators to triage and debug through natural-language conversations grounded in live telemetry. According to Bitdeer AI, the software can be deployed as a standalone platform and is integrated with NVIDIA DSX OS. Its announcement lists the telemetry scope as spanning NVIDIA GPUs, ConnectX SuperNICs, BlueField DPUs, NVLink scale-up switches, Spectrum-X East-West and North-South Ethernet, NCCL, schedulers and AI workloads.

Reported Results

NVIDIA says the platform delivers value on the key metrics AI factories focus on: longer time between interruptions, by spotting trouble before it happens; faster time to repair, by pinpointing the problem when an incident occurs; and greater insight, by filtering the flood of data modern AI infrastructure generates down to the few alerts that actually matter. The company says the platform rolls related problems up into a single actionable item so AIOps teams can shift from reactive to proactive maintenance, helping AI factories maximize GPU uptime.

Bitdeer AI reported results from NVIDIA’s internal AI factory testing: in one case, Minerva reduced mean time to repair for an NVMe power fault from a multi-day outage to six hours, and in another, it collapsed 81 noisy events into a single alert, saving 54 hours of cluster uptime. Bitdeer AI said the internal testing also showed an increase in mean time between interrupts through predictive maintenance, a decrease in mean time to repair through automated root-cause analysis, an increase in actionable insights through advanced noise reduction, and an increase in effective GPU hours that directly boosts token production.

Early Adopters and OEM Partners

IREN is among the NVIDIA partners using Minerva to optimize its AI factories. “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,” Denis Skrinnikoff, chief technology officer of IREN, said in the launch announcement.

Bitdeer AI said it now supports Minerva in its AI factory operations, describing the move as an extension of its commitment to reliable, high-yield GPU infrastructure for large-scale AI training and inference. The company said the platform’s observability, predictive maintenance and agentic troubleshooting help it catch degradations before they affect jobs, cut repair time and keep more GPUs productive for customers, and that the software is built to scale from small clusters to AI factories with hundreds of thousands of GPUs.

Lenovo said in its own GTC Berlin post that Minerva can help teams manage AI infrastructure availability and performance as environments scale, listing it alongside its Lenovo Hybrid AI Factory with NVIDIA solutions. Lenovo also said it is collaborating with IBM and NVIDIA on Sovereign AI Factory solutions intended to give customers control over their own operations.

Alongside IREN, NVIDIA named Aolani, Bitdeer AI, IBM, Jio, Lambda, Quake AI and Tesla as planning to deploy Minerva. Dell Technologies, Lenovo and Supermicro will offer the platform as part of their AI factory operations portfolios, according to NVIDIA.