Partner POV | Introducing the Dell GB300 for Secure Federal AI

Written and provided by: Dell Technologies Federal

Key takeaways

• Supports AI models with up to 1 trillion parameters.
• Keeps sensitive data contained with no external dependencies.
• Simplifies AI deployment while improving performance and resilience.

Federal agencies are under increasing pressure to operationalize AI, but the environments where AI is needed most often cannot support traditional cloud-based models. Sensitive data cannot leave controlled boundaries, connectivity is not always reliable and compliance requirements continue to tighten.

To close that gap, agencies need a different approach to AI infrastructure.

The Dell GB300 introduces that approach. It brings high-performance AI directly into secure federal environments, enabling advanced inferencing without relying on external services or constant connectivity.

A shift in how federal AI is delivered

Many AI deployments rely on centralized infrastructure and consistent connectivity. In federal environments, data handling requirements and operational constraints often require a different approach.

In many federal use cases, sending data outside a controlled environment is not an option. Even secure connections may conflict with policy or compliance requirements. At the same time, reliance on persistent connectivity introduces risk into systems that must operate continuously. The result is a growing disconnect between how AI is designed and how federal agencies actually need to use it. Local AI inference enables agencies to run AI where data already resides.

Bringing AI to the data with GB300

The Dell GB300 enables this model by delivering data center-class AI performance within controlled environments.

Built on the NVIDIA Grace Blackwell Ultra GB300 Superchip, it provides up to 20 petaflops of FP4 compute and supports models with up to 1 trillion parameters locally. This allows agencies to run advanced language models, computer vision systems and multimodal workloads without relying on external endpoints.

Beyond raw compute, the platform provides 748GB of coherent memory, combining high-bandwidth GPU memory with large-scale system memory accessible to AI workloads. This enables larger models to run on a single system and reduces deployment complexity.

For federal agencies, GB300 enables AI to run within the same environment as the data it processes.

What differentiates GB300 from traditional approaches

GB300 is designed to support production-scale AI workloads in federal environments. Its unified architecture enables large-scale models to run efficiently on a single system, reducing operational complexity and minimizing the need for distributed configurations.

Within the Dell Deskside Agentic AI platform, the GB10 supports models up to 200 billion parameters with one petaflop of FP4 compute. GB300 extends that capability to support advanced multimodal reasoning and models with up to 1 trillion parameters. The integrated AI software stack helps accelerate deployment and simplify implementation.

GB300 also gives agencies control over data, models and lifecycle management within their own environments, supporting governance, oversight and operational requirements.

Together, these capabilities help agencies deploy advanced AI while maintaining control of their infrastructure and data.

Reducing dependencies, maintaining control

Federal AI deployments often involve external services, APIs and network connections that can add complexity to operations and oversight. Local AI inference keeps model execution, inputs and outputs within the same environment as the data being processed.

By reducing dependencies on external infrastructure, agencies can simplify system architecture, maintain greater visibility across AI workflows and keep sensitive data within agency-controlled boundaries.

For organizations operating under FISMA, FedRAMP and zero trust guidance from OMB and CISA, limiting data movement can support security, compliance and governance objectives. Agencies maintain oversight of data, models and operations while deploying advanced AI capabilities where they are needed most.

Where this model is already delivering value

GB300 supports a range of federal AI use cases where performance, data control and operational resilience are critical.

Agencies responsible for processing millions of pages of regulatory, legal and healthcare documentation are applying AI to improve speed and accuracy. In many cases, tasks that previously took hours per document can now be completed in minutes. Running these models locally allows agencies to extract insights without exposing sensitive data externally.

Regulatory and financial organizations are analyzing large volumes of transactional and compliance data, often spanning years of records. Local inferencing enables more frequent and comprehensive analysis while maintaining full control and auditability.

Public safety and emergency response teams operate in time-sensitive conditions where delays can impact outcomes. Local AI supports near-real-time processing and reporting, even when connectivity is limited.

Infrastructure and operations teams managing distributed assets are deploying computer vision models locally to analyze imagery and sensor data as it is generated, reducing the need to move large datasets back to centralized systems.

Many of these environments are segmented by design, whether across agencies, data domains or operational boundaries. Because GB300 runs inference locally, each environment can host its own instance of a vetted model with no data crossing into adjacent networks. This supports segmented access requirements without sacrificing AI capability at any level.

Taken together, these use cases point to a broader shift toward deploying AI capability closer to where data is created and used.

A practical path forward for federal AI

AI is changing how federal agencies analyze information, support missions and make decisions. GB300 brings that capability directly into controlled environments, enabling agencies to run advanced AI workloads while maintaining control of their data and infrastructure.

To learn more, explore the Dell AI solutions portfolio or connect with a Federal sales specialist to request more information on our Dell Deskside Agentic AI Platform which includes the GB300.

Frequently Asked Questions

Who is the Dell GB300 designed for in a federal context?

The Dell GB300 is designed for federal data scientists, developers, analysts and IT teams who need high-performance AI capabilities within secure or controlled environments. This includes teams working with sensitive data, operating under compliance requirements or supporting mission-critical workloads where external dependencies are not viable.

What challenges does local AI inferencing solve for federal agencies?

Federal agencies often need to keep data within defined boundaries while still enabling advanced analytics. Local inferencing allows agencies to run AI models directly where the data resides, eliminating the need to send information to external services. This reduces data exposure risk, removes dependency on connectivity and supports continuity of operations in constrained environments.

How does GB300 support secure AI deployments?

GB300 enables AI workloads to run entirely within controlled environments. Model execution, input data and output results remain local to the system, which reduces external data movement and simplifies security oversight. Agencies also maintain full control over model selection, deployment and lifecycle management.

How does this approach align with federal compliance and zero trust guidance?

Running AI locally supports key principles found in federal security frameworks, including minimizing data movement and reducing reliance on external services. For agencies operating under FISMA, FedRAMP and zero trust guidance from OMB and CISA, this model can simplify authorization efforts and provide greater visibility into how AI systems handle sensitive data.

When should agencies choose local inferencing over cloud-based AI?

Local inferencing is most valuable in environments where data cannot leave a secure boundary, where connectivity is limited or inconsistent or where agencies need full control over AI systems and outputs. This includes regulated workloads, distributed operations and scenarios where reliability is critical.

What types of AI workloads can run on GB300?

The GB300 platform supports a wide range of AI use cases, including large language models for document analysis, computer vision for image and video processin and multimodal applications that combine multiple data types. With support for models up to 1 trillion parameters, it enables production-scale AI workloads to run locally without requiring external infrastructure.

Can GB300 operate in both connected and disconnected environments?

Yes. GB300 is designed to operate effectively in both connected and disconnected environments. When network access is available, it can integrate with broader IT infrastructure. When connectivity is limited or unavailable, it continues to run AI workloads locally without interruption.

Does local inferencing limit scalability or performance?

With GB300, local inferencing does not require a tradeoff between performance and control. The system delivers data center-class compute, high-bandwidth memory and the ability to run large-scale models on a single platform. This enables agencies to scale AI workloads while maintaining full control over data and execution environments.