Nvidia has unveiled new software designed to provide comprehensive monitoring and management of AI GPU fleets in data centers, with features that include the ability to track the physical location of its hardware. The platform, part of Nvidia’s broader efforts to enhance infrastructure visibility and efficiency, is opt-in and not mandatory, which may limit its effectiveness in preventing unauthorized chip transfers or smuggling. The software aggregates telemetry data from deployed GPUs and presents it through a centralized dashboard hosted on Nvidia’s NGC platform. Operators can monitor their entire GPU fleet in real time, viewing status across global or region-specific compute zones. This capability allows for precise detection of a GPU’s physical location, which could serve as a deterrent against illicit movement of high-value hardware. The system offers detailed insights into power consumption, including short-duration spikes, helping operators stay within power limits and avoid overloads. It also tracks GPU utilization, memory bandwidth usage, and interconnect health, enabling data center managers to identify performance bottlenecks such as load imbalances, bandwidth saturation, or failing links that can degrade training efficiency across large AI clusters. Thermal monitoring is another key feature. By detecting hotspots and poor airflow early, the software helps prevent thermal throttling and reduces the risk of premature hardware degradation—critical in high-density AI environments where cooling challenges are common. The platform also ensures consistency across nodes by verifying that all systems run the same software stack and operational parameters. Any discrepancies—such as mismatched drivers or configuration settings—are flagged, which is essential for maintaining reproducible training results and predictable model behavior. While this new fleet-management tool is the most advanced in Nvidia’s suite, it is not the only one. DCGM (Data Center GPU Manager) remains a powerful local diagnostic tool that provides raw GPU health data but requires users to build their own dashboards and data pipelines, limiting its ease of use despite greater customization. Base Command, meanwhile, focuses on AI workflow orchestration, job scheduling, dataset management, and collaboration—rather than deep hardware monitoring. Together, these tools form a layered approach: DCGM offers granular node-level diagnostics, Base Command manages AI workloads, and the new fleet platform brings them together into a unified, scalable solution for geographically distributed GPU deployments. While the location-tracking feature adds a new dimension to hardware security, its opt-in nature means adoption depends on customer willingness, potentially reducing its impact as a regulatory or enforcement tool.