Private AI Solutions

Private AI Solutions: Self-Hosted LLM Deployment for Regulated Industries

A private LLM is a large language model deployed on infrastructure you own or control, where no data leaves your security perimeter. A self-hosted LLM takes this further by running entirely on your on-premise servers or dedicated managed hardware, with zero reliance on third-party cloud APIs. For organizations handling controlled unclassified information, protected health information, attorney-client privileged data, or trade secrets, private AI solutions are the only architecture that satisfies compliance requirements without compromise. Petronella Technology Group, Inc. deploys private AI for business across Raleigh, North Carolina and nationwide, with complete data sovereignty and the compliance controls that CMMC, HIPAA, and SOC 2 auditors demand. We run our own private AI infrastructure. We know exactly how to build yours.

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Complete Data Sovereignty

Your data never leaves your network. Private AI models process queries, generate responses, and store results entirely within your security perimeter. No external API calls, no cloud processing, no data retention by third-party vendors. You maintain absolute control over every byte of information your AI system touches.

Air-Gapped Deployment

For defense contractors, intelligence agencies, and critical infrastructure operators, we deploy AI systems on air-gapped networks with zero internet connectivity. Models run entirely offline after initial deployment, processing classified and sensitive data without any external communication pathway that adversaries could exploit.

CMMC L2 Compliant

Private AI infrastructure satisfies CMMC Level 2 requirements for handling controlled unclassified information. Access controls, audit logging, encryption at rest and in transit, incident response procedures, and configuration management are built into the architecture; not bolted on for certification.

No Vendor Lock-In

Open-source models running on hardware you own or control means you are never dependent on a single AI vendor's pricing, policies, or continued existence. When better models emerge, you upgrade on your schedule. When regulations change, you adapt without renegotiating SaaS contracts or migrating away from proprietary platforms.

Key Takeaways

  • Private LLM deployment keeps all AI processing within your network perimeter, with zero data exposure to cloud vendors.
  • Self-hosted LLM infrastructure eliminates per-query API costs, delivering 60-80% savings over cloud AI for moderate-to-heavy usage.
  • Open-source models (Llama 3, Mistral, Qwen) have reached performance parity with commercial cloud APIs for most business applications.
  • PTG operates its own private AI fleet: 288GB VRAM GPU clusters, DGX Spark platforms, and RTX 5090 workstations running production workloads daily.
  • Air-gapped deployment is available for classified environments with zero internet connectivity.
  • Every deployment includes CMMC, HIPAA, or SOC 2 compliance controls mapped to your specific framework.

Private AI vs. Public Cloud AI: Head-to-Head Comparison

Capability Private LLM (PTG) ChatGPT / OpenAI Microsoft Copilot Google Gemini
Data stays on your network Yes No No No
CMMC L2 boundary control Full Third-party risk GCC High only Limited
Air-gapped deployment Yes No No No
Per-query cost $0 after setup $0.03-0.06/1K tokens $30/user/mo $19-30/user/mo
Custom fine-tuning Full access Limited API No Limited
Vendor lock-in None High High (M365) High (GCP)
Data used for training Never Opt-out required Policy varies Default opt-in

What Is a Private LLM and Why Does It Matter?

A private LLM is a large language model that runs exclusively on infrastructure controlled by your organization. Unlike public cloud AI services where your queries travel to shared data centers operated by third parties, a private LLM processes every request within your security perimeter. The model weights, configuration, inference logs, and all generated outputs remain under your direct control.

The private LLM market has matured rapidly. Models like Meta Llama 3 (8B to 405B parameters), Mistral and Mixtral (7B to 8x22B), and Qwen 2.5 (0.5B to 72B) deliver accuracy comparable to proprietary cloud APIs on most business tasks. These models are open-weight, meaning organizations can download, deploy, fine-tune, and modify them without licensing restrictions. The result is a fully functional AI capability that operates independently of any external vendor.

For organizations handling CUI under CMMC, PHI under HIPAA, financial data under SOC 2, or legally privileged information, a private LLM eliminates the compliance complications inherent in cloud AI adoption. No third-party data processing agreements. No vendor security assessments. No risk of policy changes retroactively affecting how your data is handled.

Self-Hosted LLM: Deployment Options and Infrastructure

A self-hosted LLM runs on servers that your organization owns, leases, or colocates, rather than on shared cloud infrastructure. Petronella Technology Group, Inc. provides three self-hosted LLM deployment models tailored to different organizational needs:

On-Premise Deployment

GPU servers installed in your data center or server room. You maintain physical control of all hardware. Ideal for organizations with existing facility infrastructure and strict data residency requirements.

Dedicated Managed Hosting

Your models run on isolated, single-tenant GPU hardware in PTG's infrastructure. No multi-tenancy. Combines data sovereignty with managed operations, including 24/7 monitoring and SLA-backed uptime.

Colocation Deployment

PTG-specified GPU servers placed in auditable colocation facilities. You own the hardware, the facility provides power, cooling, and connectivity. Full compliance documentation for your audit boundary.

We serve self-hosted LLM deployments using vLLM for high-throughput API-compatible inference, llama.cpp for efficient CPU+GPU hybrid serving, and Ollama for simplified model management. Hardware specifications range from single RTX 5090 workstations for small teams to multi-GPU EPYC servers with 288GB+ VRAM for enterprise-scale concurrent usage.

Why Private AI Is No Longer Optional for Regulated Organizations

The Data Sovereignty Crisis of Cloud AI
The adoption of cloud-based AI services has created a data sovereignty crisis that most organizations have not fully reckoned with. Every query sent to OpenAI, Anthropic, Google, or any cloud AI provider travels across networks you do not control, is processed on servers in data centers you cannot audit, and is subject to data retention policies you cannot enforce. For organizations in Raleigh, North Carolina and across the nation handling CUI under CMMC, PHI under HIPAA, financial data under SOC 2, or legally privileged information, this data exposure is not a theoretical risk; it is a compliance violation waiting to be discovered during an audit.
Documented Risks of Cloud AI Vendor Policies
The cloud AI vendor landscape compounds the problem. Major providers have repeatedly changed their data retention and training policies, sometimes retroactively. Data that was supposedly not retained for training has been used for model improvement. API logs have been stored longer than disclosed. Employee access to customer queries has been broader than documented. These are not hypothetical concerns; they are documented incidents that have driven regulatory scrutiny and enterprise risk reassessments. For organizations where data exposure has legal, regulatory, or national security implications, the risk profile of cloud AI is simply too high.
Complete Data Control With Private Deployment
Private AI solves this problem completely. When Petronella Technology Group, Inc. deploys a private AI solution, the large language model runs on infrastructure you control; either your own servers, our dedicated managed infrastructure, or colocated hardware in auditable facilities. Your data enters the model, generates a response, and is logged according to your retention policies. No external API calls. No data leaving your perimeter. No third-party vendor with access to your queries. The model weights, configuration, and deployment are under your control, and you can verify this through standard security assessments that are impossible with opaque cloud services.
We Run Our Own Private AI Infrastructure
We understand private AI deployment because we operate it ourselves. Petronella Technology Group, Inc. runs its own private AI infrastructure; including a 96-core AMD EPYC server with three NVIDIA RTX PRO 6000 GPUs providing 288GB of combined VRAM, a 24-core AMD Zen 5 workstation with an RTX 5090 and 256GB of DDR5 RAM, and NVIDIA DGX Spark clusters. Our fleet runs production AI workloads on vLLM, llama.cpp, and Ollama serving open-source models including Meta Llama, Mistral, and specialized fine-tuned variants. We host our own Nextcloud HA cluster with DRBD replication and LUKS encryption for file collaboration; no Microsoft 365 or Google Workspace dependency. This is not a sales pitch about what we could build for you. This is a description of what we already run every day.
The Improving Economics of Private AI
The economic case for private AI has improved dramatically. Open-source models have reached performance parity with commercial alternatives for most business applications. GPU hardware costs have decreased while VRAM capacities have increased. Fine-tuning techniques like LoRA and QLoRA enable domain adaptation on a single high-end GPU. The result is that a dedicated private AI deployment now costs less than multi-year SaaS subscriptions for organizations with moderate to heavy AI usage; while providing complete data control, zero per-query costs, and hardware assets that retain residual value. Our LLM fine-tuning services detail how we adapt open-source models for private deployment.

Private AI for Defense Contractors and CMMC Compliance

Why Cloud AI Complicates Your CMMC Boundary
Defense contractors handling controlled unclassified information face a specific challenge: CMMC Level 2 requires that CUI is processed, stored, and transmitted only within authorized environments with documented security controls. Cloud AI services; even those offering CMMC-compliant tiers; introduce third-party risk that complicates your authorization boundary, requires additional vendor assessments, and creates data handling dependencies outside your direct control. Private AI eliminates this complexity entirely by keeping all AI processing within your existing CMMC boundary.
CMMC L2 Controls Across All 14 Practice Domains
Petronella Technology Group, Inc. builds private AI deployments for defense contractors that satisfy CMMC L2 requirements across all 14 practice domains. Access controls enforce least privilege on model endpoints. Audit logging captures every query, response, and administrative action with tamper-evident timestamps. Encryption protects data at rest using AES-256 and in transit using TLS 1.3. Configuration management tracks every change to model weights, system prompts, and deployment parameters. Incident response procedures cover AI-specific scenarios including prompt injection attempts, data exfiltration via model outputs, and adversarial input attacks. Our CMMC compliance services and CMMC compliance guide provide comprehensive context on the regulatory landscape.
Air-Gapped Deployment for Classified Environments
Air-gapped deployment is available for organizations that require it. After initial model deployment and configuration, the AI system operates entirely offline with no network connectivity to external systems. Model updates are delivered via physically transported media following your organization's existing procedures for introducing software to isolated networks. This deployment model is essential for organizations handling classified information or operating in environments where any external network connectivity represents an unacceptable risk.

Private AI Solution Capabilities

On-Premise LLM Deployment
We deploy production-grade large language models on your infrastructure using vLLM, llama.cpp, Ollama, or custom serving frameworks optimized for your hardware. Models run on NVIDIA, AMD, or Apple Silicon GPUs with quantization strategies that balance quality and performance. We handle model selection, hardware specification, deployment automation, and performance optimization; delivering inference speeds and quality comparable to cloud APIs with complete data sovereignty.
Private RAG Knowledge Systems
Retrieval-augmented generation running entirely on private infrastructure. Your documents are embedded, indexed, and searched within your network using local vector databases. The LLM retrieves and synthesizes information from your knowledge bases without any data leaving your perimeter. This architecture is ideal for internal knowledge assistants, compliance document search, technical documentation Q&A, and any use case where the knowledge base contains sensitive information.
Air-Gapped AI for Classified Environments
Complete AI capabilities on networks with zero internet connectivity. We deploy pre-configured, pre-trained models via secure media transfer following your organization's cross-domain procedures. The system includes all dependencies; model weights, inference runtime, embedding engine, vector database, and management interface; packaged for isolated installation. Updates follow the same air-gap transfer procedures when new model versions or capability enhancements are needed.
Private Fine-Tuning & Domain Adaptation
Fine-tune open-source models on your proprietary data using techniques like LoRA, QLoRA, and full parameter training; all on private infrastructure. Your training data never leaves your network, and the resulting fine-tuned model belongs entirely to you. We specialize in domain adaptation for healthcare terminology, legal language, defense industry vocabulary, and technical specifications. See our LLM fine-tuning services for detailed methodology.
GPU Server Specification & Procurement
We specify the exact hardware configuration your private AI workload requires; CPU, GPU, RAM, storage, and networking; based on model size, concurrent user count, latency requirements, and budget. We handle vendor evaluation, procurement coordination, rack integration, and deployment automation. Our team has deployed AI workloads on everything from single RTX 5090 workstations to multi-GPU EPYC servers with 288GB of VRAM. We recommend hardware based on performance testing, not vendor relationships.
Private AI Monitoring & Management
Production AI systems require continuous monitoring. We deploy Prometheus and Grafana dashboards tracking GPU utilization, inference latency, throughput, error rates, model drift indicators, and security events. Alerting rules notify your operations team of performance degradation, hardware issues, or anomalous usage patterns. Our managed service option provides 24/7 monitoring with proactive maintenance, model updates, and performance optimization.
CMMC & HIPAA Compliance Architecture
Every private AI deployment includes security controls mapped to your specific compliance framework. Access control lists on model endpoints, AES-256 encryption on stored data, TLS 1.3 for internal communications, tamper-evident audit logs, configuration management procedures, vulnerability scanning schedules, and incident response playbooks. We produce the documentation your compliance team and auditors need; not generic security whitepapers, but specific control implementation descriptions for your deployment.
Managed Private AI Hosting
For organizations that want private AI without managing GPU hardware, we offer dedicated managed hosting on our infrastructure. Your models run on isolated hardware with no multi-tenancy; other clients' workloads never share your GPUs, memory, or storage. You get the data sovereignty benefits of private AI with the operational simplicity of a managed service. Hosting includes monitoring, maintenance, model updates, and SLA-backed availability guarantees. See our AI inference hosting services for details.

Our Private AI Deployment Process

01

Requirements & Security Assessment

We assess your compliance framework, data classification levels, user base, performance requirements, and infrastructure capabilities. This phase determines whether deployment targets your existing hardware, new on-premise servers, colocated infrastructure, or our managed hosting. Security requirements are mapped to specific controls that will be implemented in the deployment architecture.

02

Model Selection & Infrastructure Design

We benchmark candidate open-source models against your specific use cases, select the optimal model and quantization strategy, specify hardware requirements, and design the deployment architecture including networking, storage, authentication, and monitoring. For organizations requiring fine-tuning, we prepare training data pipelines and schedule GPU time on our infrastructure.

03

Deployment & Hardening

We deploy the AI system, configure security controls, implement monitoring, run performance benchmarks, and conduct security assessments. Access controls, audit logging, encryption, and compliance documentation are verified before the system accepts production traffic. User training ensures your team can interact with the system effectively and understand its capabilities and limitations.

04

Operations & Continuous Improvement

Ongoing monitoring tracks performance, security events, and usage patterns. We update models as better open-source alternatives emerge, expand capabilities based on user feedback, and maintain compliance documentation as regulations evolve. Quarterly reviews assess whether the deployment is meeting performance targets and identify opportunities to extend private AI capabilities to additional use cases.

Why Choose Petronella Technology Group, Inc. for Private AI

We Run Our Own Private AI

This is not theoretical for us. Petronella Technology Group, Inc. operates its own private AI infrastructure; 288GB VRAM GPU clusters, DGX Spark platforms, RTX 5090 workstations, and enterprise HA Nextcloud with DRBD replication and LUKS encryption. We chose private AI for ourselves for the same reasons you are considering it: data sovereignty, cost control, and zero vendor dependency.

23+ Years of Cybersecurity

Private AI is fundamentally a security architecture decision. We are a cybersecurity company first, which means every private deployment includes threat modeling, access controls, encryption, audit logging, and incident response procedures designed by security professionals; not AI engineers who learned security from a compliance checklist.

CMMC & HIPAA Expertise

We understand the specific compliance requirements that drive private AI adoption. Our team has direct experience implementing CMMC L2, HIPAA, SOC 2, NIST 800-171, and FedRAMP controls. We build AI deployments that satisfy auditors because we understand what auditors look for; from access control evidence to data handling documentation to incident response procedures.

Open-Source Model Expertise

We have deep experience with the open-source model ecosystem; Meta Llama, Mistral, Qwen, DeepSeek, and dozens of specialized variants. We benchmark, fine-tune, quantize, and deploy these models on production infrastructure daily. This hands-on operational experience means we can recommend the right model for your use case with confidence backed by data, not vendor marketing.

Hardware-Agnostic Deployment

We deploy on NVIDIA, AMD, and Apple Silicon GPUs using vLLM, llama.cpp, Ollama, and custom serving frameworks. Your hardware choice is driven by performance requirements and budget, not our vendor partnerships. We specify, procure, and deploy whatever hardware delivers the best performance per dollar for your specific workload.

Trusted Since 2002

Petronella Technology Group, Inc. has served 2,500+ businesses across Raleigh, Durham, and the Research Triangle since 2002. BBB A+ accredited since 2003. Organizations trust us with their most sensitive infrastructure and data because we have earned that trust over two decades of reliable, security-focused technology services.

Private AI Solutions FAQs

How do private AI models compare to cloud AI services like ChatGPT?
Open-source models have reached performance parity with commercial cloud APIs for most business applications. For domain-specific tasks, fine-tuned private models often outperform generic cloud services because they are trained on your specific terminology and data. The primary trade-off is that private models require GPU hardware, while cloud APIs spread hardware costs across millions of users. For organizations with compliance requirements, moderate to heavy AI usage, or data sovereignty needs, private AI is both more capable and more cost-effective long term.
What hardware is needed for private AI deployment?
Requirements depend on model size and concurrent users. Small models for focused tasks run on a single workstation-class GPU with 24GB VRAM. Mid-range deployments serving 10-50 concurrent users typically need 48-96GB of VRAM across one or two GPUs. Enterprise deployments serving hundreds of users require multi-GPU servers. We specify exact hardware configurations based on your performance requirements and budget, and can start with minimal hardware and scale as usage grows.
Can private AI work on an air-gapped network?
Yes. This is one of the primary advantages of private AI over cloud services. Once the model and serving infrastructure are deployed, no internet connectivity is required. The system operates entirely offline, processing queries and generating responses using local compute resources. Model updates are delivered via physical media following your cross-domain transfer procedures. We package complete deployments; including all dependencies; for isolated installation.
Is private AI compliant with CMMC Level 2?
Private AI is the most straightforward path to CMMC-compliant AI. By keeping all processing within your existing CMMC boundary, you avoid the third-party risk assessment, vendor authorization, and data handling complications of cloud AI services. We implement all required controls; access management, audit logging, encryption, configuration management, incident response; and produce the documentation your C3PAO assessor needs to validate compliance.
How much does private AI infrastructure cost?
Hardware costs range from a few thousand dollars for a single GPU workstation deployment to six figures for multi-GPU server clusters serving enterprise workloads. Deployment, configuration, and security hardening are project-based fees. The total cost of ownership over three to five years is typically lower than equivalent cloud AI API spending for organizations with moderate to heavy usage, with the added benefits of zero per-query costs, complete data control, and hardware asset ownership.
Can we start small and scale up later?
Absolutely. Most organizations start with a single GPU server running a quantized model for a specific use case, then expand as they validate the value and identify additional applications. The architecture we deploy is designed for horizontal scaling; adding GPUs, servers, or model variants as your needs grow. You can also start with our managed hosting and migrate to on-premise hardware once you have validated the technology and justified the capital investment.
Which AI models work best for private deployment?
The best model depends on your use case, available hardware, and performance requirements. Meta Llama 3 models excel at general-purpose tasks. Mistral and Mixtral models offer strong performance in smaller form factors. Specialized models exist for code generation, medical text, legal analysis, and multilingual applications. We benchmark multiple candidates against your specific data and select based on measured performance, not marketing claims. Fine-tuning further improves accuracy for domain-specific applications.
Do you manage the private AI system after deployment?
Yes. We offer managed services for private AI including continuous monitoring, model updates, performance optimization, security patching, and user support. For on-premise deployments, we provide remote management through secure VPN connections or on-site support depending on your security requirements. For managed hosting, operations are included in the service. We also train your internal team to handle day-to-day operations if you prefer self-management.

Explore Our AI Services

Private AI solutions are one component of Petronella Technology Group, Inc.'s comprehensive AI service portfolio. Explore related capabilities:

Last updated: March 2026. Content reflects current model availability, pricing, and compliance frameworks.

Ready to Deploy AI That Never Leaves Your Network?

Your data is your competitive advantage. Do not hand it to cloud AI vendors who process it on shared infrastructure with opaque data handling policies. Petronella Technology Group, Inc. deploys private AI solutions that deliver the full power of modern large language models while maintaining complete data sovereignty, compliance controls, and zero vendor dependency. We run private AI ourselves; we know exactly how to build it for you.

Schedule a consultation to assess your requirements, evaluate model options, and design a private AI deployment tailored to your security and compliance needs.

Call 919-348-4912 Schedule a Private AI Consultation

Serving 2,500+ Businesses Since 2002 | BBB A+ Rated Since 2003 | Raleigh, NC

About the Author

Craig Petronella, Published Author & CEO

Craig Petronella is the author of 15 published books on cybersecurity, compliance, and AI. With 30+ years of experience, he founded Petronella Technology Group, Inc. in 2002 and has helped hundreds of organizations protect their data and meet regulatory requirements. Craig also hosts the Encrypted Ambition podcast featuring interviews with cybersecurity leaders and technology innovators.

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Recommended Reading: Read our CMMC Compliance Guide to understand the requirements for handling controlled unclassified information and how private AI fits within your CMMC authorization boundary.