AI That Moves Missions Forward: Scaling priority AI workloads with secure, repeatable infrastructure

Introduction

Federal agencies can scale AI securely by aligning infrastructure, data, governance, and mission priorities from the start. This approach moves AI efforts beyond isolated pilots into a full-stack foundation that integrates high-performance compute, high-speed networking, scalable storage, and enterprise AI software and services. It aligns data, models, security, and governance so AI can operate at the edge, in the data center, and across hybrid environments.

For federal organizations, this foundation must support priority workloads and account for the practical realities of AI at scale: data quality, legacy integration, workforce readiness, security, governance, token demand, and cost predictability

Moving from AI pilots to priority workloads

AI is becoming part of the mission delivery fabric. Federal leaders have identified where AI can add value, and they are working to scale priority workloads securely and cost effectively.

As agencies expand AI across missions, they are confronting implementation challenges spanning resources, data quality and accessibility, and legacy system integration:

• 100% of federal CAIOs cited insufficient funding or resources as a barrier to AI expansion.
• 83% identified a lack of internal AI expertise as a top challenge.

To overcome these challenges, agencies need a workload-first solution path that reduces complexity, accommodates mission-specific requirements, and directly connects AI investments to mission value.

For federal agencies, priority workloads include:

• Agentic AI: Supporting administrative workflows, reporting, knowledge management, maintenance, research assistance, and decision support.
• Geospatial intelligence: Applying AI to location, imagery, mapping, planning, infrastructure resilience, environmental analysis, and mission insight.
• High-performance computing, modeling, and simulation for science: Accelerating research, scientific discovery, energy missions, and complex modeling through AI and simulation environments.
• Data engineering and mission intelligence: Turning fragmented, siloed, and unstructured data into governed datasets that can support analytics, automation, and decision-making.
• Cybersecurity and zero trust support: Identifying anomalies, prioritizing risks, accelerating response, and strengthening zero-trust environments.

These workloads depend on enabling capabilities such as high-performance inference and confidential compute. Inference allows agencies to put trained models to work at scale, delivering answers, recommendations, and actions in real time or near real time. Confidential compute helps protect sensitive data and models while they are in use, supporting stronger security and trust in regulated environments.

Managing tokenomics for agentic AI

Agentic AI can deliver major productivity gains, but always-on agents change the economics of AI. As agents run continuously across workflows, token demand becomes an important part of infrastructure planning.

Simply, workload placement matters. Cloud can be useful for testing, temporary capacity, or specialized needs, but always-on agents can quickly increase token use and costs. A token-smart strategy helps agencies decide where each workload should run based on mission sensitivity, data gravity, inference demand, latency, governance, and cost predictability.

• Agentic workloads can use 4x to 15x more tokens than standard chat interactions, and autonomous agents may drive up to 1,000x more inference demand than reasoning AI.
• In a modeled two-year analysis conducted by Signal65, Dell AI Factory with NVIDIA infrastructure reduced the cost of persistent AI agent deployments by 28% to 90%+ compared to cloud-based AI application programming interfaces.

Scaling AI with a repeatable operating model

For federal organizations, the Dell AI Factory with NVIDIA provides an integrated platform for deploying and scaling AI securely and efficiently across mission environments. It brings together Dell infrastructure and services with NVIDIA accelerated computing, NVIDIA networking, and NVIDIA AI software and models to help agencies operationalize AI across priority workloads.

For government and other security-sensitive use cases, the Dell AI Factory with NVIDIA follows the NVIDIA AI Factory for Government reference design, a validated architecture and secure deployment framework. The reference design provides guidance for security, compliance, orchestration, observability, workload support, and recommended high-security independent software vendor capabilities that can be deployed within the Dell AI Factory with NVIDIA.

This approach helps agencies avoid disconnected point solutions. Instead of building new architecture for every workload, agencies can use a common foundation to support multiple AI pathways, from deskside development and edge deployments to data center-scale inference and high-performance workloads.

Agencies gain faster time to value, stronger control, and a more consistent path from planning to operational use. They can scale priority AI workloads with greater confidence while delivering measurable return on mission through time saved, services improved, risks reduced, costs controlled, and decisions accelerated.

Building AI on governed mission data

AI depends on data that is visible, governed, and usable. Agencies need to know where data lives, how it is classified, who can access it, how it moves, and which workloads can responsibly use it.

That data foundation shapes how agencies deploy and control AI. The Dell AI Factory with NVIDIA follows the NVIDIA AI Factory for Government reference design for secure, government-grade implementation, while giving agencies the flexibility to support a range of deployment models – including fully on-premises sovereign AI environments.

Bringing AI closer to governed mission data can reduce unnecessary data movement, strengthen control, and support the economics of high-volume AI workloads. It also helps agencies maintain control of data, governance, and compliance requirements while leveraging validated AI infrastructure.

Advancing science through HPC, modeling, and simulation

AI-ready infrastructure is especially important for science, HPC, and modeling and simulation workloads, where agencies must analyze massive datasets, accelerate experimentation, and speed decision-making.

At the Department of Energy (DOE)’s Joint Genome Institute, for example, the GenomeOcean effort uses large language models to interpret and generate complex biological sequences at scale. The goal is to shorten the time between hypothesis and insight for scientists working in areas such as precision medicine, drug development, and environmental genomics. In late 2026, the initiative will get a boost from DOE’s forthcoming Doudna supercomputer, built by Dell Technologies and powered by NVIDIA Vera Rubin platform. Doudna is expected to provide more than 10 times the performance of the current flagship supercomputer.

Modeling and simulation offer a related path to mission value. Digital twins and high-fidelity virtual models allow teams to test tradeoffs, evaluate scenarios, generate synthetic data, and reduce risk before acting in the physical world. At the Naval Postgraduate School, modeling and simulation teams are using shared digital twins and what-if testing to shorten planning cycles by months or years and support faster, more confident decisions. Open, modular infrastructure helps teams collaborate at speed and scale.

Embedding governance, security, and sovereignty by design

Scaling AI requires governance and security from the start. Agencies must address data protection, model oversight, auditability, zero trust, and sovereign AI considerations.

As AI moves into production, governance must also extend to model selection, inference operations, agent behavior, token consumption, and workload utilization. Confidential compute can support this foundation by helping protect sensitive data and models while they are being processed. Hardware-enforced security, such as NVIDIA Confidential Computing, combined with clear policies and monitoring, helps agencies scale AI without increasing risk.

Validated security capabilities integrated with the Dell AI Factory with NVIDIA can further strengthen this foundation. Fortanix Confidential Computing and Data Security Manager, for example, can help highly regulated organizations train and deploy sensitive AI workloads with stronger data protection.

The goal is to deploy AI that remains transparent, controlled, trusted, and aligned with mission requirements.

Building the foundation for return on mission

AI moves missions forward when agencies can operationalize it securely, responsibly, and at scale. That requires a workload-first strategy supported by a validated infrastructure foundation, strong data governance, trusted security, and predictable economics.

With the Dell AI Factory with NVIDIA, agencies can move priority AI workloads into governed production with greater confidence. The platform follows the NVIDIA AI Factory for Government reference design for secure, government-grade deployment and supports Dell sovereign AI environments for organizations that need greater control over data, governance, compliance, and infrastructure. The result is a secure, repeatable foundation for return on mission – saving time, improving services, reducing risk, controlling costs, and accelerating decisions.