Private AI DeploymentYour Data Never Leaves
Deploy production-grade AI on your own infrastructure with complete data sovereignty. Petronella Technology Group handles model selection, hardware sizing, security hardening, and ongoing management for organizations that cannot risk sending sensitive data to third-party AI services.
Private AI Infrastructure Services
GPU Infrastructure Deployment
Full GPU server provisioning with containerized model serving, load balancing, and high-availability configuration for on-premises or private cloud.
Model Selection and Optimization
Evaluate models against your use case and optimize with quantization, KV cache tuning, and batching strategies for maximum throughput.
Security Hardening
Network isolation, API authentication, input validation, output filtering, prompt injection defenses, and comprehensive audit logging.
Fine-Tuning and RAG
Custom fine-tuning on your domain data with retrieval-augmented generation pipelines grounded in your documents.
Air-Gapped Environments
Deployment in CMMC and classified environments with no internet connectivity. All dependencies packaged for offline installation.
24/7 Monitoring
Continuous monitoring of model health, inference latency, GPU utilization, and security events with proactive alerting.
Built For Regulated Industries
Frequently Asked Questions
What hardware do we need?
A 7B parameter model runs on a single NVIDIA A100 or H100 GPU. Larger models require 2-4 GPUs. We handle all hardware sizing and procurement recommendations.
How does private deployment cost compare to API pricing?
At 500K+ tokens per day, private deployment breaks even within 6-12 months. At 5M+ daily, costs are 60-80% less annually than equivalent API spend.
Can you deploy in air-gapped environments?
Yes. We regularly deploy in CMMC and classified environments with zero internet connectivity.
What models do you deploy?
Llama 3.1, Mistral, Qwen 2.5, Phi, and others. We benchmark options against your specific use case before recommending.
How do we get started?
Call (919) 348-4912 or schedule a consultation. We assess your requirements and design a private deployment plan.
How do I deploy a private enterprise AI cluster on-premise for a regulated business?
Work through six stages. First, define the data boundary: which systems hold regulated data such as CUI, PHI, or financial records, and which frameworks apply (CMMC Levels 1, 2, or 3, HIPAA, DFARS 252.204-7012). Second, size GPU hardware to the models you plan to run. Third, isolate the cluster on a segmented VLAN or a full air gap. Fourth, deploy open-weight models on an inference stack you control so prompts and outputs never leave your network. Fifth, layer on role-based access control, encryption at rest and in transit, and audit logging mapped to your framework's controls. Finally, validate the deployment against those controls before production use. Petronella Technology Group designs, builds, and operates private AI clusters for regulated businesses end to end.
Is on-premise AI better for CMMC and HIPAA compliance than cloud AI APIs?
For regulated data, yes. A cloud AI API means a third party processes your data under its own terms, which complicates or breaks the compliance boundary for CUI and PHI. An on-premise cluster keeps every prompt, document, and model output inside infrastructure you control and can present to an assessor or auditor. That data sovereignty is why defense contractors and healthcare organizations increasingly standardize on private AI for any workload that touches regulated information.
Explore More
Deploy AI That Stays Private
Schedule a free consultation to discuss your private AI deployment requirements, compliance needs, and infrastructure sizing.