In March 2026, the Government Accountability Office found that government-wide AI guidance addressed only two of 10 expert-identified privacy challenges associated with federal AI use. That gap becomes more significant as agencies move from experimentation to operational deployment under OMB M-25-21, which requires testing, ongoing monitoring, and human oversight for high-impact systems.
The question is no longer whether agencies can deploy AI. It is where different classes of AI workload belong.
Most generative AI workloads belong in the cloud, on the platforms and partners agencies already use. A specific subset does not. Content governed by classification or policy boundaries, disconnected operating environments, and increasingly, sustained agentic workloads all create requirements that can make cloud deployment difficult, expensive, or both.
Those workloads need a system that runs where the data is already allowed to live, produces an audit trail an approver accepts, and allows agencies to operate AI without continually forecasting token consumption.
That is what DMI built Q Box for.
When On-Premises Is the Right Path
The first signal is the physical boundary.
For some workloads, the question is not which model to use. It is where the data is permitted to reside.
Content governed by classification requirements, FISMA controls, NIST SP 800-53 security controls, DoD impact levels, or other policy constraints often must remain inside an approved environment. In those cases, deploying AI inside an existing enclave can simplify governance by keeping data, access controls, and oversight inside a boundary agencies already manage.
The second signal is the cost curve.
Agentic AI changes the economics of inference.
Gartner projects that AI inference cost per agentic workflow will rise more than fivefold through 2028 as these systems consume increasing volumes of tokens for retrieval, reasoning, tool use, and self-correction. Falling token prices are increasingly offset by the amount of computation agentic workflows require.
What begins as a single request can become a chain of interconnected tasks, each carrying forward the context of the previous step.
The shift matters because agencies are increasingly evaluating AI as infrastructure rather than a single application. A pilot may involve dozens of users. A production deployment may involve thousands. As adoption expands, the question becomes less about the cost of an individual prompt and more about the economics of operating AI as an ongoing service.
For CIOs, Chief AI Officers, and program leaders, that can mean the difference between managing known infrastructure capacity and continually forecasting expanding token consumption.
On-premises inference does not make AI free. It makes AI budgetable.
Boundary requirements and economics are different problems, but they often lead to the same question:
How do you deploy AI inside an environment you already trust, govern, and operate?
That is the role Q Box was designed to fill.
Built for Accredited Environments
Q Box is a self-hosted generative AI solution designed for workloads that need to operate inside an existing accredited environment.
It runs on a reference hardware platform built around an AI-optimized GPU and ships with DMI's pre-integrated stack of open-source AI components. The architecture is open-weight and model-agnostic, enabling agencies to select and replace models as requirements evolve.
Because Q Box operates inside the environment an agency already controls, data remains where policy requires it to remain.
That changes more than where the model runs. It gives agencies direct control over the AI operating environment, from the data it can access to the records available for oversight and review.
Role-based permissions govern access, and audit logs capture activity. Air-gapped deployment is the default operating model rather than a security enhancement added later. Q Box is designed for environments operating under federal governance frameworks such as NIST SP 800-53, NIST SP 800-207, FISMA, and DoD security requirements.
For federal buyers, this supports two aspects of sovereignty:
- Where data resides
- Who controls the model environment
That control also has implications for how agencies evaluate and govern AI.
As the Office of Management and Budget (OMB) continues to establish policy requirements for federal AI acquisition and deployment, agencies increasingly need visibility into how AI systems are configured, evaluated, and overseen.
OMB M-26-04 requires new large language model solicitations to include contractual provisions related to truth-seeking and ideological neutrality. By giving agencies control over model selection, prompts, configurations, access, and logs, Q Box provides more direct visibility into the evidence used to evaluate and govern an AI system.
The next question is where that degree of control delivers enough value to warrant an on-premises deployment. The answer depends on the constraints surrounding the workload.
Where It Fits
Consider a federal benefits program reviewing applications, case records, policy documents, and supporting materials. Staff may need to locate the policy that applies to a particular case, compare information across multiple records, or identify exceptions that require additional review.
Generative AI can help accelerate that work by searching, summarizing, and analyzing information that is already available to program staff. The challenge is that the underlying records often contain sensitive information, operate under strict access controls, and must remain inside an approved environment.
The workload also changes as adoption grows. A pilot may support a small group of users working on a narrow set of tasks. A production deployment may support teams across an entire program, with each request triggering retrieval, reasoning, and multiple steps of analysis. Over time, the challenge becomes not only where the data resides, but also how the organization manages AI usage at scale.
In that situation, the question is not whether AI can help. It is how to apply AI while maintaining control over data, access, oversight, and long-term operating costs.
By training the locally hosted model on their own data, agencies can improve relevance and accuracy while keeping the workload inside the environment they already control. The agency determines which data the system can access, who can use it, which models support the workflow, and which records are retained for oversight and review.
The benefits example is only one problem shape. The same pattern appears in defense operations, security analysis, compliance programs, software delivery teams, and other environments where workloads operate under one or more of three conditions:
- Strict data boundaries
- Limited or unreliable connectivity
- Economics that become difficult to sustain under token-based pricing
Once an agency identifies a workload with those characteristics, the challenge shifts from placement to execution: selecting the first use case, configuring the environment, connecting the relevant data, and establishing an operating model.
That is where the deployment approach matters.
From Placement to Execution
Identifying the right workload is only the first decision.
Once an agency determines that a workload requires greater control over data, connectivity, governance, or operating costs, the conversation shifts from where AI belongs to how it will be deployed and operated.
That includes selecting an initial use case, connecting the relevant data, establishing governance, and determining who will maintain the environment as models, policies, and mission requirements evolve.
DMI approaches that process through the D3 framework:
Discover identifies the use case, data sources, and governance requirements.
Define configures the environment and validates security and workflow requirements.
Deliver deploys the environment, performs local model training and RAG optimization, and activates the first workload.
Deployment is only part of the challenge. Models change, capacity requirements evolve, and support needs continue after the first workload goes live. Agencies can operate Q Box independently or use DMI managed services to support ongoing operations.
What This Changes for Federal Leaders
For agencies operating under strict data boundaries, limited connectivity, or growing AI consumption, the decision is no longer simply whether to use AI.
It is how to apply AI while maintaining control over where data resides, how systems are governed, and how operating costs are managed over time.
Q Box gives agencies a path to deploy and operate AI inside the environments they already govern, using models, workflows, and operating practices that can evolve as requirements change while delivering more relevant and accurate inference outcomes.
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Keep the workload where policy allows it. Keep the economics where leadership can manage them. Q Box was built for both.
