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Air-Gapped AI for Defense: NexTech Solutions Deploys a VRLA Tech 4U Rackmount Workstation
NexTech Solutions LLC — a defense technology company building mission-critical communications, counter-UAS, and edge compute systems for the U.S. military and federal government — needed AI that could work with sensitive internal documents without routing anything through a commercial cloud service. Their solution: a VRLA Tech 4U Rackmount Workstation running open-weight language models on their own hardware, in their own environment, with no data leaving the building.
| 192 GB Total ECC GDDR7 VRAM | 24 cores Threadripper 9960X / 48 threads | 128 GB DDR5 ECC system memory |
The Challenge
For a defense technology company like NexTech — building edge compute systems, counter-UAS solutions, and classified communications infrastructure for military and federal clients — the risk model around AI is fundamentally different from a commercial enterprise. The same productivity tools that help a typical team draft faster and summarize longer — cloud-based AI APIs — require sending text to infrastructure the organization doesn’t own or control. For NexTech, whose work includes classified communications networks, tactical data systems, counter-drone systems, and deployable edge compute for military programs, that’s a non-starter.
The team wanted AI-assisted document drafting, summarization, and document question-and-answer — the same productivity gains available to any knowledge-work team — without the data residency exposure that comes with commercial cloud AI. That meant running capable open-weight models in-house, on hardware they control, with no telemetry and no internet dependency once deployed.
The core requirement: Run capable open-weight AI language models entirely on internal infrastructure — no cloud, no external API calls, no data leaving the controlled environment under any operating condition.
What makes this achievable today is the maturity of open-weight models. Leading models now match or exceed GPT-4-class performance on many real-world tasks — and they run entirely on hardware you own, without a network connection, with no usage data sent anywhere. The limiting factor is GPU VRAM: a 70B-parameter model at full FP16 precision requires roughly 140GB of GPU memory. That constraint drove the hardware selection.
The Build
VRLA Tech configured a 4U Rackmount Workstation built around two NVIDIA RTX PRO 6000 Blackwell Max-Q GPUs. With 96GB of ECC GDDR7 per card, the system
