On-Premise AI Solutions Private Models on Your Infrastructure
Run LLMs, ML pipelines, and AI applications entirely within your data center. No data leaves your perimeter. No API fees that scale with headcount. CMMC, HIPAA, and ITAR compliant by design, not by vendor promise.
On-Premise AI vs. Cloud AI
Complete data sovereignty, zero per-user fees, and air-gap capability that cloud AI cannot provide.
Data Sovereignty & Security
- Your prompts, documents, and model outputs never leave your firewall
- Air-gapped and SCIF-ready configurations for classified environments
- CMMC, HIPAA, and ITAR compliance built into the architecture
- No third-party data retention, logging, or model training on your data
Cost & Control
- Zero per-user API fees. One-time hardware cost with near-zero marginal cost per query.
- Payback period of 4 to 8 months for mid-size deployments vs. cloud AI
- Fine-tune open-source models on your proprietary data for domain-specific AI
- Sub-millisecond network latency for real-time applications
On-Premise AI Solutions
End-to-end deployment from hardware design to production model serving.
GPU Server Design & Deployment
Custom AI server builds with NVIDIA RTX 5090, RTX PRO 6000, A100, and H100 GPUs matched to your model and throughput requirements.
Private LLM Deployment
Run Llama, Mistral, Qwen, and custom models on your infrastructure with vLLM, llama.cpp, or Ollama. Private GPT solutions for knowledge workers.
RAG Pipeline Development
RAG systems that index your documents on private infrastructure. Employees get AI-powered answers without data leaving your network.
Model Fine-Tuning
Fine-tune open-source models on your contracts, procedures, medical records, or engineering data. Create AI that understands your domain.
Enterprise AI Security
Security controls engineered into every layer: data leakage prevention, access controls, model integrity monitoring, and compliance governance.
Managed AI Operations
24/7 monitoring, model updates, security patching, performance optimization, and capacity planning. You focus on using AI while we keep it running.
How We Deploy On-Premise AI
Assess workloads, compliance needs, and infrastructure
Design GPU server architecture and network topology
Build, harden, and deploy hardware on-site
Deploy models with inference optimization
Integrate with existing applications and workflows
Train your team and provide ongoing support
Built For
Frequently Asked Questions
How much does on-premise AI infrastructure cost?
Dual-GPU inference servers start around $15,000. Multi-GPU training rigs with 192 GB to 384 GB VRAM for 70B+ parameter models range from $40,000 to $120,000. Most deployments achieve payback in 4 to 8 months compared to cloud AI API costs.
What open-source models do you deploy?
We deploy any open-source model including Llama 3, Mistral, Qwen, DeepSeek, and domain-specific models. Model selection depends on your use case, VRAM capacity, and performance requirements. We benchmark candidates against your workload before deployment.
Can on-premise AI work in air-gapped environments?
Yes. We build fully air-gapped deployments with offline model repositories, no internet dependency, and no outbound network paths. These configurations meet CMMC, ITAR, and SCIF requirements for classified and controlled environments.
How does on-premise AI compare to Copilot or ChatGPT Enterprise?
On-premise AI eliminates per-user fees (Copilot: $30/user/month), keeps all data on your network, enables full model customization, and provides compliance by design. A 200-person org spending $72,000/year on Copilot can deploy on-premise AI for a one-time hardware investment. See our Copilot alternative page for detailed comparison.
What power and cooling do GPU servers require?
A single multi-GPU server can draw 4,000W to 5,000W requiring dedicated 30A or 50A circuits. We conduct site assessments to verify power availability, cooling capacity, and rack density limits before specifying hardware. For organizations with facility constraints, our GPU server hosting eliminates the need for on-site infrastructure.
Ready for On-Premise AI?
Get a custom deployment proposal with hardware specs, cost analysis, and compliance documentation plan.