Private GPT & Private LLM Deployment

Private GPT for Business: Self-Hosted AI That Keeps Your Data Off the Internet

Private GPT is a self-hosted large language model deployment that gives your organization ChatGPT-level AI capabilities without sending a single byte of data to external servers. Your questions, your documents, your proprietary knowledge, it all stays on hardware you control. Petronella Technology Group, Inc. deploys private GPT systems for businesses that cannot afford the data exposure risk of cloud AI. With 24 years of cybersecurity experience, 2,500+ clients served, and zero breaches since 2002, we build private AI infrastructure that satisfies the strictest compliance requirements in healthcare, defense, legal, and financial services.

919-348-4912 Deploy Your Private GPT

Your Data Never Leaves Your Network • Zero Breaches Since 2002 • BBB A+ Since 2003

Key Takeaways

Private GPT runs entirely on your infrastructure. No data touches external servers, period
Modern open-source models (Llama 3.1, Mistral, Qwen 2.5) match or exceed GPT-4 performance on most business tasks
Fine-tune the model on your company's documents, SOPs, and domain knowledge for answers specific to your business
PTG handles the full deployment: hardware specification, model selection, fine-tuning, RAG integration, and security hardening
Compliance-ready from deployment: HIPAA, CMMC, SOC 2, and NIST 800-171 controls built in

What Is Private GPT and Why Does Your Business Need It?

Every time an employee pastes a customer contract into ChatGPT, asks Claude to summarize a financial report, or uploads a medical record to a cloud AI tool, that data enters systems your organization does not control. OpenAI, Google, Anthropic, and Microsoft each have different data retention policies, training opt-out procedures, and liability frameworks. These policies change without notice. For businesses handling sensitive data, whether that means protected health information under HIPAA, controlled unclassified information under CMMC, attorney-client privileged communications, or proprietary financial models, cloud AI creates unacceptable and often compliance-violating risk.

Private GPT eliminates that risk entirely. A private GPT deployment is a large language model running on servers you own, in a data center you control, on a network that never connects to the public internet for inference. Your employees get the same conversational AI capabilities they use in ChatGPT: document summarization, content generation, data analysis, code assistance, research synthesis, and question answering. The difference is that every query and every response stays within your security perimeter. No data leaves your building. No third party can access your prompts. No vendor trains future models on your proprietary information.

This is not a theoretical distinction. In April 2023, Samsung banned ChatGPT after engineers leaked semiconductor source code and internal meeting notes through the platform. Major law firms have issued similar prohibitions. Defense contractors working with CUI cannot use cloud AI tools at all under CMMC requirements. Private GPT gives these organizations a compliant path to AI adoption rather than an outright ban that leaves productivity gains on the table while competitors advance.

How PTG Deploys Private GPT

We handle the full deployment lifecycle, from hardware specification through production operation. The process begins with understanding your use cases: What will your team use the AI for? How many concurrent users do you need to support? What response time is acceptable? Which documents and knowledge bases should the model access? These answers determine the hardware requirements, model selection, and architecture decisions.

Models We Deploy

We work with the leading open-source model families, selecting the right model for your specific use case, hardware, and performance requirements:

Meta Llama 3.1 (8B/70B/405B) - Strongest general-purpose open model. Excellent for document summarization, content generation, analysis, and reasoning tasks. The 70B variant runs well on dual-GPU workstations; the 405B requires multi-node clusters
Mistral and Mixtral (7B/8x22B) - Outstanding code generation, multilingual support, and instruction following. Mixtral's mixture-of-experts architecture delivers high throughput at lower GPU cost
Qwen 2.5 (7B/72B) - Strong performance on structured data, mathematical reasoning, and Asian language tasks. Competitive with Llama 3.1 at equivalent parameter counts
DeepSeek-V3 - Excellent for coding, technical documentation, and analytical tasks. Cost-effective inference due to efficient architecture

Infrastructure Options

Private GPT deployments run on three infrastructure configurations, depending on your security requirements, budget, and scale:

On-Premise Server

Dedicated GPU server installed in your office or data center. Full air-gap capability. Best for organizations with strict data residency requirements (CMMC, ITAR, classified environments). We specify, build, ship, and install the hardware, then deploy and configure the AI stack.

Private Cloud

Dedicated GPU instances in a private cloud environment (single-tenant, not shared infrastructure). Network-isolated from other tenants and the public internet. Suitable for organizations that need scalability without the capital expense of physical hardware. We manage the infrastructure and guarantee data isolation.

Hybrid

Sensitive workloads run on-premise (document analysis with PHI, CUI processing, proprietary data). General-purpose queries route to a private cloud instance for scalability. This architecture balances security with cost and performance, giving you the best of both models.

Private GPT vs. ChatGPT Enterprise vs. Azure OpenAI vs. Self-Hosted Open-Source

The key difference is who controls your data and how much you control the model.

Feature	PTG Private GPT	ChatGPT Enterprise	Azure OpenAI	DIY Open-Source
Data leaves your network	Never	Yes (OpenAI servers)	Azure datacenters	Never
Vendor trains on your data	No	No (opted out)	No	No
Fine-tunable on your domain data	Full control	No	Limited	Full control
Air-gap capable	Yes	No	No	Yes
HIPAA/CMMC compliance	Built in	BAA available	BAA available	You must build
RAG with your documents	Included	Limited	Yes	You must build
Professional deployment and support	Included	SaaS support	Azure support	None
Predictable monthly cost	Yes	Per seat	Usage-based	Yes (hardware)
Security hardening included	24 years expertise	OpenAI's controls	Azure's controls	DIY

Industries That Need Private GPT

Healthcare. HIPAA requires that AI systems processing protected health information maintain strict access controls, audit logging, and encryption. Cloud AI tools processing clinical notes, patient communications, or billing data create compliance liability. Private GPT lets healthcare organizations use AI for clinical documentation, medical research synthesis, and administrative automation without PHI ever leaving the secure network. See our healthcare AI consulting services for specialized healthcare deployments.

Legal. Attorney-client privilege demands that sensitive case materials, legal strategies, and client communications remain within the firm's control. Cloud AI services that process legal documents introduce third-party access risks that could compromise privilege. Private GPT enables document review, contract analysis, legal research, and brief drafting with the assurance that confidential materials stay within the firm's infrastructure.

Defense and Government. CMMC and NIST 800-171 require that controlled unclassified information (CUI) be processed only on authorized systems. Cloud AI platforms do not satisfy CUI handling requirements. Defense contractors and government agencies deploy private GPT for proposal writing, technical documentation, intelligence analysis, and internal knowledge management on air-gapped or CMMC-compliant infrastructure.

Financial Services. SOC 2, PCI DSS, and GLBA regulations govern how financial data is processed and stored. Private GPT allows banks, investment firms, insurance companies, and accounting practices to use AI for risk modeling, fraud analysis, compliance monitoring, and client communication without exposing sensitive financial data to external systems.

What You Get with a PTG Private GPT Deployment

A typical deployment includes: hardware specification and procurement (or private cloud provisioning), operating system installation and security hardening, model selection and deployment, RAG (retrieval-augmented generation) integration with your documents, a web-based chat interface for your team, role-based access controls, audit logging, fine-tuning on your domain-specific data (optional), and 90 days of post-deployment support. We also provide training for your team so they can use the system effectively from day one.

Craig Petronella, CMMC Registered Practitioner and Licensed Digital Forensic Examiner, oversees the security architecture for every private GPT deployment. With 30+ years of IT experience and 15 published books on cybersecurity, Craig ensures that your private AI system meets the same security standards we apply to mission-critical IT infrastructure for healthcare systems, defense contractors, and financial institutions.

Private GPT FAQ

How does private GPT performance compare to ChatGPT?

For most business tasks (summarization, content generation, data analysis, Q&A, code assistance), current open-source models perform comparably to GPT-4. Llama 3.1 405B matches or exceeds GPT-4 on the majority of standardized benchmarks. Smaller models (70B, 8B) handle routine tasks well while requiring significantly less hardware. The performance gap that existed in 2023 has narrowed dramatically. When you add RAG (retrieval-augmented generation) with your company's documents, private models often outperform cloud AI because they access your specific knowledge base rather than relying on general training data.

What hardware do we need for private GPT?

Hardware requirements depend on the model size and concurrent user count. A small deployment (1-10 users, 8B model) runs on a single workstation with one NVIDIA RTX 4090 or RTX PRO 6000 GPU ($5,000-$12,000 hardware cost). A mid-size deployment (10-50 users, 70B model) requires a dual-GPU server with NVIDIA A100 or H100 GPUs ($25,000-$60,000). Enterprise deployments (50+ users, multiple models) use multi-node GPU clusters. We provide detailed hardware specifications during the assessment phase, and we can build custom servers or provision private cloud instances based on your needs.

Can we train the private model on our company's documents?

Yes, through two methods. RAG (retrieval-augmented generation) connects the model to your document library at query time, so it can answer questions using your SOPs, policies, contracts, technical documentation, and internal knowledge base. This is the fastest approach and does not modify the base model. Fine-tuning trains the model on your data to change its behavior, tone, and domain expertise permanently. Fine-tuning is ideal when you need the model to consistently use industry-specific terminology, follow your writing style, or perform specialized tasks. Most deployments start with RAG and add fine-tuning for specific use cases where it delivers measurable improvement.

How much does a private GPT deployment cost?

Total cost depends on deployment scale. A small on-premise deployment (1-10 users) runs $15,000-$30,000 including hardware, deployment, RAG setup, and 90 days of support. Mid-size deployments (10-50 users) range from $40,000-$80,000. Enterprise deployments start at $100,000+. Private cloud deployments have lower upfront costs ($5,000-$15,000 setup) with monthly infrastructure fees of $1,000-$5,000. Compared to ChatGPT Enterprise at $60/user/month, private GPT becomes more cost-effective at roughly 20+ users over a 24-month period, while providing stronger security, full customization, and no per-user licensing fees.

Is private GPT HIPAA and CMMC compliant?

When deployed by PTG, yes. The private deployment model inherently satisfies data residency requirements since no data leaves your infrastructure. We add the compliance-specific controls on top: role-based access controls, AES-256 encryption at rest and in transit, comprehensive audit logging with configurable retention periods, automatic session management, and incident response procedures. For HIPAA environments, we implement Technical Safeguards including access controls, audit controls, integrity controls, and transmission security. For CMMC environments, we align controls with NIST 800-171 requirements for CUI protection. Our 24 years of compliance consulting ensures these controls are implemented correctly, not just checked off a list.

Deploy Your Private GPT

Stop choosing between AI productivity and data security. Petronella Technology Group, Inc. deploys private GPT systems that give your team powerful AI capabilities without exposing sensitive data to third parties. Call us to discuss your use case, compliance requirements, and deployment options.

Call 919-348-4912 Schedule Private GPT Consultation

Your Data Stays Yours • 2,500+ Clients Since 2002 • BBB A+ Since 2003

Last Updated: March 2026