On-Prem AI for Defense Contractors: The Complete Deployment Guide
On-prem AI for defense contractors means running large language models and AI agents entirely on hardware you own, inside your existing CMMC and ITAR compliance boundary, so no controlled data ever leaves your network. For contractors handling CUI or ITAR technical data, public AI services like ChatGPT are effectively off-limits: DFARS 252.204-7012 and ITAR 120.17 treat uncontrolled cloud transmission as a reportable incident or an unauthorized export. This guide covers the architecture, hardware, models, and rollout plan that let a mid-sized defense manufacturer get real AI capability without expanding its assessment scope or risking a State Department violation.
Why Public Cloud AI Fails the Defense Compliance Test
The problem is data flow, not model quality. When an engineer pastes a drawing note or a DD Form 254 excerpt into a consumer AI tool, that data transits third-party infrastructure with no DFARS 252.204-7012 coverage, no FedRAMP Moderate baseline, and no guarantee against foreign-person access, which ITAR treats as a deemed export. Even FedRAMP-authorized offerings like Azure OpenAI in Azure Government solve only part of the problem: you still pay per token, you still depend on connectivity, and ITAR data requires US-persons-only support commitments that most SaaS AI contracts do not make. On-prem deployment collapses all of these questions because the model weights, prompts, and outputs never leave hardware inside your accredited boundary.
Reference Architecture: Hardware, Models, and Serving Stack
A practical on-prem AI stack for a 50-500 person defense manufacturer fits in one or two rack units. Open-weight models such as Llama 3.1 70B, Qwen 2.5 72B, or Mistral Large 2 quantized to 4-bit run capably on 2x NVIDIA L40S or a single H100; smaller 8B-14B models handle classification and extraction on a single RTX 6000 Ada. Budget roughly $30,000-$90,000 for GPU hardware, or $60,000-$150,000 for a redundant dual-node setup.
- Inference serving: vLLM or NVIDIA Triton behind an internal API gateway with mTLS
- Retrieval layer: a local vector database (Qdrant, pgvector, or Milvus) indexing ERP and document data
- Identity: SSO via your existing Active Directory or Entra ID, with per-group data scoping
- Logging: full prompt and response audit trail retained per your CMMC AU control family
Fitting AI Inside Your CMMC and ITAR Boundary
The fastest compliant path is to place AI servers inside the same enclave that already holds your CUI, so your existing SSP, boundary diagram, and NIST SP 800-171 controls extend to the new system rather than creating a second assessment scope. Document the AI stack as a new system component in your SSP, map it against the relevant 800-171 controls (especially AC-3, AU-2, SC-8, and SC-28), and update your data flow diagrams before your C3PAO assessment. For ITAR technical data, restrict model and index access to verified US persons and record that restriction in your Technology Control Plan.
- Add the inference and vector-store servers to your existing SSP hardware inventory
- Enforce US-persons-only administrative access for any system indexing ITAR technical data
- Encrypt model storage and vector indexes at rest with FIPS 140-2 validated modules
- Prohibit outbound internet from inference nodes; pull model updates through a scanned transfer zone
A Realistic 90-Day Rollout Plan
Most contractors overestimate the difficulty. Weeks 1-2: pick two high-value use cases, typically quote drafting from RFQ packages and contract clause analysis, and size hardware against them. Weeks 3-6: stand up the inference stack, connect read-only pipelines from SyteLine, Infor LN, or your PLM system, and build retrieval indexes over specs, past quotes, and quality records. Weeks 7-10: pilot with 5-10 users, measure time saved per task, and tune prompts and retrieval. Weeks 11-13: update the SSP, brief your assessor or compliance lead, and expand to production. Contractors following this cadence typically see first measurable ROI, often 8-15 hours saved per estimator per week, before the quarter ends.
How Netray Deploys On-Prem AI for Defense Manufacturers
Netray specializes in exactly this deployment: on-prem AI agents wired into Infor SyteLine, CloudSuite Industrial, Infor LN, and Baan environments at aerospace and defense manufacturers. We deliver the full stack, GPU sizing, model selection, vLLM serving, ERP retrieval pipelines, and the SSP documentation package your C3PAO will ask for, in a fixed-scope 90-day engagement. Client results include quote turnaround cut from 10 days to 2, engineering document search dropping from hours to seconds, and zero expansion of CMMC assessment scope because everything runs inside the existing enclave. Every Netray deployment includes an ITAR-aligned Technology Control Plan addendum and US-persons-only support.
Frequently Asked Questions
Can defense contractors use ChatGPT for work involving CUI?
No. Pasting CUI into consumer ChatGPT transmits controlled data to infrastructure outside your DFARS 252.204-7012 boundary, which can constitute a reportable cyber incident and, for ITAR technical data, an unauthorized export. Contractors need either a FedRAMP-authorized government cloud AI service with contractual CUI coverage or an on-prem deployment where prompts and outputs never leave company-owned hardware.
How much does an on-prem AI system cost for a defense manufacturer?
A single-node system running a quantized 70B open-weight model costs roughly $30,000-$90,000 in GPU hardware (for example 2x NVIDIA L40S or one H100), plus implementation services. A redundant dual-node production setup runs $60,000-$150,000. Compared with per-token cloud pricing and compliance risk, most contractors reach breakeven within 12-18 months on labor savings alone.
Does adding on-prem AI expand my CMMC assessment scope?
Not if you place the AI servers inside your existing CUI enclave and document them as new components in your current SSP. The inference servers, vector database, and gateway inherit the enclave's NIST SP 800-171 controls. You update the hardware inventory, data flow diagrams, and relevant control implementations rather than creating a new assessment boundary.
Key Takeaways
- 1Why Public Cloud AI Fails the Defense Compliance Test: The problem is data flow, not model quality. When an engineer pastes a drawing note or a DD Form 254 excerpt into a consumer AI tool, that data transits third-party infrastructure with no DFARS 252.204-7012 coverage, no FedRAMP Moderate baseline, and no guarantee against foreign-person access, which ITAR treats as a deemed export.
- 2Reference Architecture: Hardware, Models, and Serving Stack: A practical on-prem AI stack for a 50-500 person defense manufacturer fits in one or two rack units. Open-weight models such as Llama 3.1 70B, Qwen 2.5 72B, or Mistral Large 2 quantized to 4-bit run capably on 2x NVIDIA L40S or a single H100; smaller 8B-14B models handle classification and extraction on a single RTX 6000 Ada.
- 3Fitting AI Inside Your CMMC and ITAR Boundary: The fastest compliant path is to place AI servers inside the same enclave that already holds your CUI, so your existing SSP, boundary diagram, and NIST SP 800-171 controls extend to the new system rather than creating a second assessment scope. Document the AI stack as a new system component in your SSP, map it against the relevant 800-171 controls (especially AC-3, AU-2, SC-8, and SC-28), and update your data flow diagrams before your C3PAO assessment.
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