Private LLM DeploymentAI That Never Leaves Your Servers
Run large language models on your own infrastructure with complete data sovereignty. 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.
Private LLM Services
Model Selection and Sizing
Evaluate Llama, Mistral, Phi, Qwen against your performance, hardware, and compliance needs. Benchmark before recommending.
Infrastructure Deployment
GPU server provisioning, containerized model serving (vLLM, llama.cpp, TGI), load balancing, and high-availability configuration.
Fine-Tuning and RAG
Custom fine-tuning on your domain data with LoRA, QLoRA, or full fine-tuning. RAG pipelines grounded in your documents.
Security Hardening
Network isolation, API authentication, input validation, output filtering, prompt injection defenses, and audit logging.
Performance Optimization
Quantization, KV cache optimization, batching strategies, and model pruning to maximize throughput on your hardware.
24/7 Monitoring
Continuous monitoring of model health, inference latency, GPU utilization, and error rates with proactive alerting.
Cloud API vs. Private LLM
Data Sent to Provider
Every prompt and response traverses third-party infrastructure outside your security boundary.
Per-Token Pricing
Costs scale linearly with usage, making high-volume workloads increasingly expensive.
Limited Customization
Restricted to provider API options without ability to fine-tune on your domain data.
100% On Your Servers
All data stays within your infrastructure. Zero external exposure.
Fixed Infrastructure Cost
Unlimited inference. Organizations processing 1M+ tokens daily see 60-80% cost reduction.
Full Fine-Tuning
Custom models trained on your data that understand your industry terminology and workflows.
Frequently Asked Questions
What hardware do we need for a private LLM?
A 7B parameter model runs on a single NVIDIA A100 or H100. 70B models require 2-4 GPUs. We handle all hardware sizing and procurement recommendations.
Which models work best for private deployment?
Llama 3.1, Mistral, and Qwen 2.5 lead for general-purpose use. Smaller fine-tuned models often outperform larger ones for specialized tasks at lower cost.
Can you deploy in air-gapped environments?
Yes. We regularly deploy in CMMC and classified environments with no internet connectivity. All dependencies packaged for offline installation.
How does cost compare to API pricing?
At 500K+ tokens/day, private deployment breaks even within 6-12 months. At 5M+/day, costs are 60-80% less annually.
How do we get started?
Call 919-348-4912 or schedule a consultation to discuss your private LLM requirements.
Explore More
Deploy AI That Stays Private
Schedule a free consultation to discuss your private LLM requirements. We assess your use case, recommend models, and size the infrastructure.