What does 'air-gapped' mean for AI deployment?

Air-gapped means the entire AI stack runs on your servers with zero external API calls. Models are served locally via NVIDIA NIM, Ollama, or vLLM on your own GPUs. No data ever leaves your network — not for inference, not for logging, not for telemetry. You have complete data sovereignty.

What models can run in an air-gapped deployment?

Any open-weight model: Llama, Mistral, Gemma, Phi, Falcon, and more. NVIDIA NIM optimizes inference for NVIDIA GPUs. You can also run quantized models on smaller hardware. New models are loaded offline via secure media transfer — no internet connection required.

What hardware do I need?

The minimum is an Ubuntu server with NVIDIA GPUs (A100, H100, L40S, or RTX series). The stack runs on any CUDA-capable hardware. For smaller deployments, quantized models can run on a single GPU. Large institutions typically use multi-GPU servers or small clusters.

Is ibl.ai's air-gapped deployment ITAR/FedRAMP/HIPAA compliant?

Yes. Because no data leaves your network, the deployment inherits your existing security posture. Air-gapped mode is designed for ITAR, FedRAMP, HIPAA, FERPA, and classified environments. All audit logs, model weights, and user data stay within your infrastructure.

How do updates work without internet access?

Updates are delivered via secure offline packages. Our team provides signed update bundles that you transfer to your air-gapped environment via approved media. Model updates, platform patches, and new features are all handled through this offline update process.

How long does an air-gapped deployment take?

A typical deployment takes 2-4 weeks from hardware provisioning to production agents. Our forward-deployed engineers handle the entire setup on-site. We start with a free 30-minute assessment to map your infrastructure and compliance requirements.

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Air-Gapped AI

Run ibl.ai's entire Agentic OS on air-gapped Ubuntu servers with NVIDIA GPUs. Local models via NIM, Ollama, or vLLM. Zero external API calls, complete data sovereignty for your agency. On ibl.ai you own all the code and the data, run it model-agnostic across any LLM, and pay with no per-seat pricing — so you can deploy anywhere, from your own cloud to a fully air-gapped network. No need to choose build vs. buy — you get both.

Sector:Higher Education Corporate Small Business K-12 Government Legal Financial Services Healthcare

Your Code. Your Data.Full ownership, zero vendor lock-in

Any LLM, Your ChoiceAir-gapped or any cloud provider's

85% Lower CostSignificantly cheaper at scale

AI TransformationPre-existing and custom agents

Air-Gapped AI - Local Models, Maximum Control for Government

Deploy ibl.ai's full Agentic OS on air-gapped infrastructure where no data ever leaves your agency enclave. Models run locally on Ubuntu servers with NVIDIA GPUs via NIM, Ollama, or vLLM.

ibl.ai's forward-deployed engineers install the entire stack on your hardware. You get the same AI agent capabilities as our cloud deployment—mission support, workforce training, citizen services—with zero external API calls, complete data sovereignty, and ATO-boundary preservation.

What This Is

Air-Gapped AI is ibl.ai's on-premise deployment option. The entire Agentic OS—agent runtime, model serving, vector databases, orchestration layer—runs on Ubuntu servers inside your enclave with no internet connectivity required after initial setup.

Models are served locally through NVIDIA NIM, Ollama, or vLLM on your NVIDIA GPUs. You choose from models by NVIDIA, Meta (Llama), Google (Gemma), Microsoft (Phi), Mistral, and others. Every inference request stays within your security perimeter and ATO boundary.

ibl.ai's forward-deployed engineers configure the stack, optimize model performance for your hardware, integrate with your agency systems, and transfer full operational knowledge to your team.

Every configuration file, every model weight, every integration adapter belongs to your agency.

Why Air-Gapped for Government

Complete Data SovereigntyNo data leaves your enclave. No API calls to external AI providers. Mission data, personnel records, and classified information stay within your security perimeter at all times.

ATO Boundary PreservationAir-gapped deployment keeps AI within your existing Authorization to Operate boundary. No new external connections to authorize. No cloud dependencies to document. Simplifies security assessment.

Classification-ReadySuitable for IL4, IL5, and higher environments. Models run entirely within your enclave. No data exfiltration risk through external API calls. Compatible with cross-domain solutions.

Model Choice and FlexibilityRun any open model that fits your GPUs. Switch between Llama, Gemma, Phi, Mistral, or NVIDIA NeMo models without changing agent configurations. No vendor lock-in to any single model provider.

Same Capabilities as CloudAir-gapped deployment runs the full ibl.ai Agentic OS. AI agents for training, mission support, citizen services, analytics—every feature works identically to the cloud version.

Supported Models and Inference Engines

NVIDIA NIMGPU-optimized inference microservices for maximum throughput on NVIDIA hardware. Supports Llama, Mistral, and NVIDIA NeMo models with TensorRT-LLM acceleration. Best for high-throughput production workloads.

OllamaLightweight model serving for rapid deployment and testing. Supports a broad catalog of open models with simple configuration. Ideal for development environments and smaller-scale deployments.

vLLMHigh-performance inference engine with PagedAttention for efficient memory management. Supports continuous batching for maximum GPU utilization. Production-grade serving for large-scale deployments.

Model CatalogMeta Llama (8B, 70B, 405B), Google Gemma (2B, 7B, 27B), Microsoft Phi (3.5, 4), Mistral (7B, 8x7B, Large), NVIDIA NeMo models, and any Hugging Face-compatible model. New models added as they release.

Infrastructure Requirements

Operating SystemUbuntu 22.04 LTS or later. Standard server installation with NVIDIA drivers and CUDA toolkit. No specialized OS or kernel modifications required.

GPU RequirementsNVIDIA GPUs with sufficient VRAM for your chosen models. A single A100 80GB runs Llama 70B. Smaller models like Phi-3.5 or Gemma 7B run on consumer-grade GPUs. We right-size recommendations to your workload.

NetworkNo internet connectivity required after initial setup. Internal network access to agency systems (HRIS, LMS, IdP) for integrations. All model weights and dependencies are pre-loaded during installation.

StorageSSD storage for model weights, vector databases, and agent state. Capacity depends on the number of models deployed. Typical installations require 500GB to 2TB of fast storage.

Security and Compliance

ITAR CompatibleAir-gapped deployment meets ITAR requirements for agencies handling export-controlled data. No data transmission to foreign servers. Complete physical and logical isolation.

FedRAMP / NIST 800-53 AlignedOn-premise deployment within your ATO boundary. All NIST 800-53 controls are addressable locally. No shared infrastructure, no multi-tenant risks, no cloud provider dependencies.

IL4/IL5 ReadyDesigned for deployment in Impact Level 4 and 5 enclaves. All AI processing stays within the enclave boundary. Compatible with existing cross-domain and data guard architectures.

Continuous MonitoringLocal audit logging integrates with your SIEM and continuous monitoring infrastructure. Every agent interaction, model inference, and tool invocation is captured for security review.

Deployment Options

Single Server (Agency Enclave)Entire stack on one Ubuntu server with NVIDIA GPUs in your enclave. Suitable for program offices, mission units, or pilot programs. Simple to operate within existing security controls.

Multi-Node ClusterDistributed deployment across multiple servers for higher throughput and redundancy. Kubernetes orchestration with Helm charts. Scales to agency-wide usage within your enclave.

Hybrid (Enclave + GovCloud)Classified workloads in your enclave, unclassified agents in GovCloud. Consistent configurations across both. Cross-domain solutions with consistent agent behavior and security policies.

What You Own

Complete Agentic OS installation on your hardware with all agent configurations and model settings documented

Local model weights for all deployed models—pre-downloaded and optimized for your GPU hardware

Inference engine configurations (NIM, Ollama, or vLLM) tuned for your specific hardware and workload

Agency system integration adapters (HRIS, training systems, IdP) with full source code

Infrastructure as Code (Ansible/Helm) for repeatable deployments and disaster recovery

Operational runbooks covering model updates, GPU monitoring, backup procedures, and troubleshooting

ATO documentation support—architecture diagrams, data flow maps, NIST 800-53 control matrices

Engagement Model

Infrastructure Assessment (1 week):Evaluate your server hardware, GPU inventory, enclave network topology, and integration requirements. Right-size model recommendations to your compute capacity and ATO boundary.

Installation and Configuration (2-4 weeks):Forward-deployed engineers install the Agentic OS, configure inference engines, load model weights, build agency integrations, and validate the full stack within your enclave.

Agent Development (2-3 weeks):Build your first set of AI agents—workforce trainers, mission assistants, citizen-service aids. Configure guardrails, knowledge bases, and tool integrations specific to your mission.

Knowledge Transfer (1-2 weeks):Train your operations team on model management, agent configuration, GPU monitoring, and security procedures. Your team operates independently after handoff.

Get Started

Hardware Assessment:Free 30-minute session to evaluate your existing enclave infrastructure, GPU capacity, and ATO requirements.

Proof of Concept:Deploy the Agentic OS on a single server within your security boundary to validate the approach before committing to full-scale deployment.

Agency Deployment:Complete air-gapped installation with agency integrations, agent library, ATO documentation support, operational procedures, and knowledge transfer.