Fleet adds NVIDIA AI workstation management to enterprise IT workflows

Unified management for AI hardware

Enterprises that use NVIDIA AI workstations can now integrate these systems into their existing device management workflows. This update from Fleet Device Management allows IT and security teams to oversee specialized AI hardware in the same way they manage standard company laptops. The announcement was made during the NVIDIA GTC Berlin event.

The new support specifically includes NVIDIA RTX PRO workstations and the NVIDIA DGX Station. These devices are designed for heavy-duty tasks like large AI development, data science, and running inference. By bringing them into the Fleet ecosystem, companies can treat these powerful machines as part of their wider IT estate rather than isolated units.

Fleet aims to solve the management demands that arise when organizations bring high-performance computing into professional environments. As developers and engineers run advanced workloads locally, the company needs a way to maintain oversight. This integration ensures that the hardware supporting these tasks remains visible to the people responsible for the company's digital infrastructure.

Visibility into security and software

Fleet provides a central dashboard that gives IT teams a clear view of the operating systems, hardware, and installed software on every device. This includes a detailed look at security controls and known vulnerabilities across the entire network. For companies using NVIDIA hardware, this means they can see exactly what is happening on an AI workstation from a single location.

The platform allows teams to assess where AI compute is actually being deployed within the organization. They can verify whether these specific devices comply with the internal requirements set by the company. This level of transparency is important for maintaining a consistent security posture as AI adoption grows across different departments.

By using a single platform, organizations can avoid the complexity of using different tools for different types of hardware. Instead of having one system for Windows laptops and another for specialized AI stations, they can use one interface. This helps ensure that security standards are applied uniformly across all types of computing power.

Git-based workflows for AI governance

One of the core features of the Fleet platform is its support for Git-based workflows. This means that policies, configurations, and deployment waves can be managed using the same version control systems that software engineers already use. It allows for a more structured and reviewable way to handle hardware updates and changes.

These workflows enable organizations to apply auditable processes to AI hardware throughout its entire lifecycle. Teams can track every change made to a device and, if necessary, reverse those changes if an issue arises. This creates a paper trail for how configurations are updated and who authorized those updates.

The ability to manage deployment waves is particularly useful for large companies. It allows them to roll out new software or configurations to a small group of AI workstations before pushing them out to the rest of the fleet. This reduces the risk of a single configuration error affecting every developer at once.

Impact on developers and engineers

The update is designed to support the specific needs of developers, creators, and engineers who require local AI compute. Many of these professionals need to run agentic AI and other complex workloads that require the high performance of NVIDIA hardware. Fleet makes it easier for these teams to get the tools they need while staying within company guidelines.

NVIDIA vice president of Enterprise Platforms, Chris Marriott, noted that the company is bringing powerful AI computing directly to these teams. He stated that Fleet's support provides a unified approach to deploying and managing those systems. This suggests a focus on making the transition to high-end AI hardware as seamless as possible for the end user.

For the IT department, this means less friction when approving requests for new hardware. Because the devices can be managed through existing workflows, the barrier to entry for high-performance AI tools is lowered. It balances the need for raw power with the necessity of corporate governance and security.

Standardizing the AI workstation

The inclusion of the NVIDIA DGX Station is a significant part of this update. This specific hardware is built to run AI locally, supporting a wide range of tasks from data science to inference. By supporting this hardware, Fleet is acknowledging the shift toward local AI processing in the enterprise.

Fleet's CEO and co-founder, Mike McNeil, emphasized that enterprises should be able to manage AI workstations from the same place as their other devices. He argued that this should be done using consistent visibility and controls. This philosophy aims to eliminate the silos that often form when specialized hardware is introduced to a corporate network.

Moving forward, this integration helps organizations stay ahead of the demands of modern AI development. As more companies adopt NVIDIA's ecosystem, having a tool that bridges the gap between high-performance hardware and standard IT management becomes essential. It provides a path for scaling AI capabilities without sacrificing the security of the company's IT environment.