On-Premise AI Deployment: Private Models Running on Your Infrastructure

On-premise AI refers to running artificial intelligence workloads on hardware that your organization owns and controls, rather than sending data to a cloud provider's servers. This includes large language model inference, retrieval-augmented generation (RAG) pipelines, computer vision processing, speech recognition, and any other AI task that would otherwise require an API call to OpenAI, Anthropic, Google, or another cloud AI vendor. The models run on GPU servers located in your data center, your server closet, or a colocation facility where you maintain physical control of the hardware.

The shift toward on-premise AI deployment accelerated in 2024 and 2025 as organizations realized that cloud AI comes with three fundamental problems. First, data privacy: every prompt you send to a cloud AI provider travels across the internet and is processed on someone else's servers. Even with data processing agreements and enterprise tiers, you are trusting a third party with your most sensitive information. Second, cost: cloud AI APIs charge per token, per request, or per user. A 200-person organization paying $30 per user per month for Microsoft Copilot spends $72,000 per year, and that cost grows linearly with headcount. Third, control: when a cloud provider changes pricing, deprecates a model, or alters rate limits, your organization has no recourse. On-premise AI eliminates all three problems.

For organizations handling controlled unclassified information (CUI), protected health information (PHI), International Traffic in Arms Regulations (ITAR) data, or attorney-client privileged materials, on-premise AI is not just preferable. It is often the only compliant option. Cloud AI providers cannot guarantee that your data will not be used for model training, stored in jurisdictions you have not approved, or accessed by the provider's employees during support or debugging operations. Self-hosted AI infrastructure gives you verifiable, auditable control over every byte of data that touches your AI systems.

Petronella Technology Group, Inc. has been building and deploying private AI systems since the earliest days of enterprise LLM adoption. We understand the GPU hardware landscape, the open-source model ecosystem, the inference optimization stack, and the security controls required to run AI in regulated environments. Whether you need a single inference server for a small team or a multi-node GPU cluster for organization-wide deployment, PTG designs and delivers on-premise AI solutions that perform, scale, and comply with your regulatory obligations.

The foundation of every on-premise AI deployment is the GPU server. Modern large language models require substantial VRAM (video memory) to load model weights and generate responses. A 7-billion parameter model like Llama 3 8B needs approximately 16 GB of VRAM at full precision, while a 70-billion parameter model requires 140 GB or more. The GPU you choose determines which models you can run, how fast they generate responses, and how many concurrent users your system can support.

PTG builds on-premise AI servers across the full NVIDIA product line. For small to mid-size deployments serving 10 to 50 users, dual NVIDIA RTX 5090 configurations with 64 GB total VRAM deliver strong performance on 7B to 13B parameter models at a hardware cost of $8,000 to $15,000. Mid-range deployments for 50 to 200 users typically use RTX PRO 6000 or A6000 GPUs with 192 GB to 384 GB total VRAM, supporting 34B to 70B parameter models with concurrent request handling. These builds range from $30,000 to $60,000. Large enterprise deployments use NVIDIA A100 or H100 GPUs with 320 GB to 640 GB of VRAM, running multiple models simultaneously and supporting hundreds of concurrent users. These configurations range from $80,000 to $250,000 depending on scale. Visit our GPU server hosting page if you prefer to avoid on-site hardware and instead host dedicated GPU servers in a managed facility.

Beyond the GPUs themselves, on-premise AI infrastructure requires careful attention to CPU selection, system memory, NVMe storage for model loading, networking for multi-node deployments, power delivery, and cooling. A single multi-GPU server can draw 2,000W to 5,000W depending on configuration, requiring dedicated 30A or 50A circuits. PTG conducts site assessments to verify power availability, cooling capacity, and rack density limits before specifying hardware. For organizations with facility constraints, our custom AI workstation configurations provide powerful AI capabilities in a standard desktop form factor that does not require a dedicated server room.

PTG builds every server in-house, runs burn-in testing for 72 hours, benchmarks against your target workloads, and hardens the operating system before shipping. We install Ubuntu Server or Rocky Linux with minimal attack surface, configure GPU drivers and CUDA libraries, deploy your inference stack (vLLM, Ollama, TGI, or custom), set up monitoring agents, and document the complete configuration. When the server arrives at your location, it is ready for production. No additional engineering required on your end.

Air-gapped AI is the strictest form of on-premise AI deployment. In an air-gapped configuration, the AI system has no connection to the internet, no outbound network paths, and no ability to communicate with any system outside the secured environment. All models, libraries, dependencies, and updates are loaded via physical media or a unidirectional data transfer mechanism. This architecture is required for environments handling classified national security information, ITAR-controlled technical data, and certain categories of controlled unclassified information (CUI) under CMMC Level 2 and Level 3 requirements.

PTG builds air-gapped AI systems that include offline model repositories, local package mirrors, and pre-staged update bundles. We configure the entire software stack to operate without any internet dependency. Model weights, tokenizer files, embedding models, and all supporting libraries are loaded during initial provisioning and updated through a controlled media transfer process. The system generates no outbound DNS queries, no NTP synchronization to external servers, and no telemetry or usage data that could leak information about your operations. For SCIF (Sensitive Compartmented Information Facility) deployments, PTG works with your security team to ensure the AI system meets all physical security and TEMPEST requirements for the facility classification.

Beyond air-gapped deployments, PTG designs on-premise AI architectures that satisfy CMMC, HIPAA, and ITAR compliance requirements for organizations that need network connectivity but still require strict data controls. For CMMC compliance, we implement the access controls, audit logging, system integrity monitoring, and data flow restrictions required to protect CUI while using AI. For HIPAA-covered entities, we configure AI systems with the administrative, physical, and technical safeguards required under the Security Rule, ensuring that protected health information processed by AI systems receives the same protections as PHI in any other system. For ITAR compliance, we verify that AI systems processing defense articles or technical data meet the export control requirements that prohibit access by non-U.S. persons. Our compliance team works alongside our AI engineers to build systems that satisfy both technical performance requirements and regulatory obligations.

Every on-premise AI deployment from PTG includes documentation that maps the system architecture to applicable compliance framework controls. This documentation supports your compliance audits by providing clear evidence of how data flows through the AI system, what controls protect that data, and how the system is monitored and maintained. Whether you are responding to a CMMC assessment, a HIPAA audit, or an ITAR compliance review, the documentation package we provide gives auditors the technical detail they need to validate your AI system's compliance posture.

The cost of on-premise AI deployment depends on three factors: the size of the models you need to run, the number of concurrent users, and your compliance requirements. A straightforward single-server deployment for a small team running 7B to 13B parameter models starts at $15,000 for hardware plus $5,000 to $10,000 for deployment and configuration services. Mid-range deployments for departments or mid-size companies running 34B to 70B parameter models typically land between $40,000 and $80,000 for hardware and services. Large enterprise deployments with multi-node GPU clusters, high-availability configurations, and comprehensive compliance documentation range from $120,000 to $300,000.

The ROI calculation for on-premise AI is compelling once you compare it to ongoing cloud AI costs. Consider a 200-person organization paying $30 per user per month for Microsoft Copilot. That is $72,000 per year, or $360,000 over five years. An on-premise deployment at $60,000 for hardware and $15,000 for deployment services reaches payback in roughly 12 months and saves $285,000 over the same five-year period. If your organization uses cloud AI APIs (OpenAI, Anthropic, Google) at higher volume, the payback period shrinks further. Organizations spending $10,000 or more per month on API calls typically recover their hardware investment in 4 to 6 months.

Ongoing costs for on-premise AI are modest compared to cloud alternatives. Electricity for a typical multi-GPU server runs $150 to $400 per month depending on your local utility rates and the server's power draw. PTG's managed operations service covers monitoring, patching, model updates, and support on a monthly retainer. Hardware typically has a useful life of 3 to 5 years for AI workloads before a GPU generation upgrade makes sense.

PTG provides detailed cost proposals for every engagement that break down hardware costs, deployment services, and ongoing operations fees. We also provide a cloud-vs-on-premise cost comparison tailored to your specific usage patterns so you can evaluate the investment against your current spending. For organizations that need on-premise AI capabilities but want to spread the hardware cost over time, leasing arrangements are available through our hardware partners. Contact us for a custom quote that matches your use case, user count, and compliance requirements.

The open-source AI model ecosystem has matured to the point where locally hosted models can match or exceed cloud AI performance for most enterprise use cases. Meta's Llama 3, Mistral AI's Mistral and Mixtral models, Alibaba's Qwen series, Microsoft's Phi models, and DeepSeek's offerings all deliver strong performance across text generation, summarization, code completion, question answering, and document analysis. These models are available under permissive licenses that allow commercial use, fine-tuning, and deployment on your own infrastructure without ongoing licensing fees.

PTG evaluates models against your specific use cases before recommending a deployment configuration. We run benchmark tests using your actual data (or representative samples) to measure response quality, generation speed, and accuracy. If a 7B parameter model handles your workload with acceptable quality, we deploy the smaller model and save you $30,000 or more in GPU hardware. If your use case requires a 70B+ parameter model for nuanced reasoning or complex document analysis, we build the infrastructure to support it. Model selection is a technical decision, and PTG brings the benchmarking methodology and real-world deployment experience to make it correctly.

For organizations that need specialized capabilities beyond general-purpose text generation, PTG deploys domain-specific models for code generation (CodeLlama, StarCoder, DeepSeek Coder), medical applications (MedLlama, BioMistral), legal analysis, financial modeling, and multilingual processing. We also build multi-model architectures where different models handle different tasks within a single deployment. A typical enterprise configuration might run a fast 7B model for simple queries, route complex reasoning tasks to a 70B model, and use a specialized embedding model for semantic search. This multi-model approach optimizes both cost and performance. Learn more about our approach to private AI solutions for enterprise environments.