Private LLM Deployment: AI That Never Leaves Your Servers
Private LLM deployment is the process of running large language models on your own infrastructure, keeping all data, prompts, and outputs within your security boundary. Petronella Technology Group deploys production-grade private LLMs for organizations that cannot risk sending sensitive data to third-party AI services. We handle model selection, hardware sizing, fine-tuning, and ongoing optimization, combining 24+ years of cybersecurity expertise with deep AI engineering to ensure your private AI is both performant and secure.
CMMC RP-1372. 24+ years in cybersecurity and AI. Free consultation.
Key Takeaways
- 62% of enterprises cite data privacy as the top barrier to AI adoption (McKinsey 2024). Private deployment eliminates this concern entirely.
- Private LLMs are mandatory for CMMC, HIPAA, and classified environments where sending data to third-party APIs violates regulatory requirements.
- Self-hosted models eliminate per-token API costs, providing unlimited inference at fixed infrastructure cost. Organizations processing 1M+ tokens daily see 60-80% cost reduction.
- Petronella deploys models on NVIDIA GPU infrastructure with enterprise-grade security, monitoring, and failover, not hobbyist setups.
What We Deliver
Model Selection and Sizing
We evaluate your use case against available models (Llama, Mistral, Phi, Qwen, and others) and recommend the best fit for your performance requirements, hardware budget, and compliance needs.
Infrastructure Deployment
Full GPU server provisioning, containerized model serving (vLLM, llama.cpp, TGI), load balancing, and high-availability configuration. On-premises, private cloud, or air-gapped environments.
Fine-Tuning and RAG
Custom fine-tuning on your domain data to improve accuracy for your specific use cases. Retrieval-Augmented Generation (RAG) pipelines that ground model outputs in your actual documents and knowledge bases.
Security Hardening
Network isolation, API authentication, input validation, output filtering, prompt injection defenses, and audit logging. Every deployment follows NIST and CIS security baselines.
Performance Optimization
Quantization, KV cache optimization, batching strategies, and model pruning to maximize throughput on your hardware. We benchmark and tune until your deployment meets production SLAs.
Monitoring and Support
24/7 monitoring of model health, inference latency, GPU utilization, and error rates. Proactive alerting and rapid response to performance degradation or security events.
LLM Deployment Approaches Compared
| Approach | Commercial API | Petronella Private LLM |
|---|---|---|
| Data privacy | Data sent to provider | 100% on your servers |
| Compliance eligible | Varies, often no | HIPAA, CMMC, FedRAMP |
| Cost model | Per-token, scales linearly | Fixed infrastructure cost |
| Customization | Limited to API options | Full fine-tuning, custom models |
| Latency | Network dependent | Local, sub-100ms |
| Availability | Vendor SLA | Your control, 99.9%+ |
Led by Craig Petronella
Craig Petronella founded Petronella Technology Group in 2002 with 30+ years of cybersecurity expertise. A CMMC Registered Practitioner (RP-1372), Craig combines deep security knowledge with AI engineering to deliver solutions that are both technically sound and practically secure.
Frequently Asked Questions
What hardware do we need for a private LLM?
Which models work best for private deployment?
Can you deploy in an air-gapped environment?
How does private LLM cost compare to API pricing?
Can we fine-tune the model on our data?
Related Services
Deploy AI That Stays Private
Schedule a free consultation to discuss your private LLM requirements. We will assess your use case, recommend models, and size the infrastructure.
Petronella Technology Group, Inc.
5540 Centerview Dr. Suite 200, Raleigh, NC 27606
Phone: 919-348-4912