A comprehensive guide to deploying private, on-premise AI and Large Language Models (LLMs) for organizations that need AI capabilities without sending sensitive data to cloud providers. Includes hardware selection, model comparison, security hardening, and governance frameworks.
- Why Private AI?
- Use Cases for On-Premise AI
- Architecture Overview
- Hardware Requirements
- Inference Frameworks
- Model Selection Guide
- Deployment Options
- Security and Compliance
- Performance Optimization
- Cost Analysis
- Implementation Roadmap
- Templates and Resources
- About Petronella Technology Group
The AI revolution is transforming every industry, but for organizations handling sensitive data -- healthcare providers, defense contractors, financial institutions, legal firms -- sending confidential information to cloud AI providers creates unacceptable risks.
Private AI deployment keeps your data within your control while delivering the full power of modern language models, image generation, and AI-assisted workflows.
- Data sovereignty: Your data never leaves your infrastructure. No third-party access, no cloud provider data retention policies to worry about
- Regulatory compliance: Meet HIPAA, CMMC, SOC 2, ITAR, and other frameworks that restrict data handling to authorized systems
- Zero data leakage: Eliminate the risk of sensitive information being used to train external models or appearing in other users' outputs
- Predictable costs: No per-token pricing surprises. One-time hardware investment plus electricity delivers unlimited inference
- Customization: Fine-tune models on your proprietary data for domain-specific accuracy
- Availability: No dependency on external API availability, rate limits, or service outages
- Air-gap capable: Deploy in fully air-gapped environments for classified or highly sensitive workloads
Organizations using cloud AI services face these documented risks:
| Risk | Impact | Example |
|---|---|---|
| Data retention | Provider stores your prompts and outputs | Samsung engineers leaked proprietary code via ChatGPT (2023) |
| Model training | Your data may train the provider's models | OpenAI's default data usage policy includes training |
| Breach exposure | Provider breach exposes your conversations | ChatGPT conversation history leak (March 2023) |
| Compliance violations | Sending CUI/PHI to unauthorized systems | HIPAA violation for PHI sent to cloud AI without BAA |
| Vendor lock-in | Dependency on single provider's pricing and availability | OpenAI API pricing changes, Anthropic rate limits |
| Shadow AI | Employees use unauthorized cloud AI tools | 60% of employees admit using AI tools without IT approval |
- Clinical documentation: AI-assisted note generation from patient encounters
- Medical coding: Automated ICD-10/CPT code suggestion from clinical notes
- Research analysis: Processing research data with PHI without cloud exposure
- Patient communication: AI-generated summaries and explanations in plain language
- Document analysis: Processing classified or CUI documents with AI assistance
- Intelligence analysis: Pattern recognition across large document sets
- Code generation: AI-assisted development for controlled environments
- Training materials: Generating scenario-based training content
- Contract analysis: Reviewing and summarizing legal documents
- Discovery support: Processing large document sets for relevant information
- Brief generation: AI-assisted legal writing with client confidentiality
- Compliance review: Automated policy and regulation analysis
- Risk analysis: Processing proprietary financial models and data
- Report generation: Automated financial reporting from internal data
- Customer communication: Personalized communication drafting
- Fraud detection: Pattern analysis on transaction data
A production private AI deployment typically follows this architecture:
+------------------+
| Load Balancer |
+--------+---------+
|
+--------------+--------------+
| |
+-------+-------+ +-------+-------+
| Inference | | Inference |
| Server 1 | | Server 2 |
| (GPU Node) | | (GPU Node) |
+-------+-------+ +-------+-------+
| |
+-------+-------+ +-------+-------+
| Model Storage | | Vector DB |
| (NFS/S3) | | (RAG Pipeline) |
+----------------+ +----------------+
| |
+-------+-----------------------------+-------+
| Internal Network |
| (Isolated VLAN / Air-gapped) |
+---------------------------------------------+
- Inference server(s): GPU-equipped machines running the LLM inference engine
- Model storage: Shared storage for model weights (10-100+ GB per model)
- API gateway: Manages authentication, rate limiting, and request routing
- Vector database: Enables Retrieval-Augmented Generation (RAG) for domain-specific knowledge
- Monitoring: GPU utilization, latency, throughput, and error tracking
- Network isolation: Dedicated VLAN or air-gapped network for the AI infrastructure
Hardware selection is the most critical decision in private AI deployment. The right hardware depends on your model size, concurrent user count, and latency requirements.
See guides/hardware-selection.md for the complete hardware selection guide.
| Use Case | Model Size | Recommended GPU | VRAM | Est. Cost |
|---|---|---|---|---|
| Small team (5-10 users) | 7-8B params | NVIDIA RTX 4090 (24GB) | 24 GB | $1,600 |
| Department (10-30 users) | 13-14B params | NVIDIA RTX 5090 (32GB) | 32 GB | $2,000 |
| Department (30-50 users) | 32-70B params | NVIDIA A6000 (48GB) | 48 GB | $4,500 |
| Enterprise (50-200 users) | 70B+ params | NVIDIA A100 (80GB) | 80 GB | $15,000 |
| Enterprise (200+ users) | 70B+ params | Multi-GPU (2-8x A100/H100) | 160-640 GB | $30,000-$250,000 |
| Budget option | 7-8B params | AMD RX 7900 XTX (24GB) | 24 GB | $900 |
| Component | Minimum | Recommended |
|---|---|---|
| CPU | 8 cores, AVX2 support | 16+ cores, AVX-512 |
| RAM | 32 GB | 64-128 GB |
| GPU VRAM | 16 GB | 24-48 GB |
| Storage | 500 GB NVMe SSD | 2+ TB NVMe SSD |
| Network | 1 Gbps | 10 Gbps (multi-GPU) |
| Power | 700W PSU | 1000W+ PSU |
Three major open-source frameworks dominate private AI inference. See guides/model-comparison.md for detailed comparison.
Best for: Getting started quickly, small teams, development environments
Ollama provides the simplest deployment experience with a Docker-like pull-and-run model.
# Install
curl -fsSL https://ollama.com/install.sh | sh
# Pull and run a model
ollama pull llama3.1:8b
ollama run llama3.1:8b "Summarize this contract clause..."
# Serve API
ollama serve # Runs on localhost:11434Pros: Easy setup, model management, automatic GPU detection, cross-platform Cons: Single-GPU only, limited batching, higher latency at scale
Best for: Production deployments, high-throughput serving, multi-GPU
vLLM uses PagedAttention for efficient memory management and supports continuous batching for maximum throughput.
# Install
pip install vllm
# Serve a model
vllm serve meta-llama/Llama-3.1-8B-Instruct \
--host 0.0.0.0 --port 8000 \
--tensor-parallel-size 2 # Multi-GPUPros: Highest throughput, multi-GPU support, OpenAI-compatible API, production-ready Cons: More complex setup, NVIDIA-focused (ROCm support improving)
Best for: CPU inference, edge deployment, maximum hardware flexibility
llama.cpp is a highly optimized C++ implementation supporting CPU, CUDA, ROCm, Metal, and Vulkan backends.
# Build from source
git clone https://github.com/ggml-org/llama.cpp
cd llama.cpp && cmake -B build -DGGML_CUDA=ON && cmake --build build
# Serve a model
./build/bin/llama-server \
-m models/llama-3.1-8b-instruct.gguf \
--host 0.0.0.0 --port 8080Pros: Runs everywhere (CPU, any GPU), smallest memory footprint (quantization), fastest single-user latency Cons: Lower throughput for concurrent users, manual model conversion (GGUF format)
| Feature | Ollama | vLLM | llama.cpp |
|---|---|---|---|
| Setup complexity | Very easy | Moderate | Moderate |
| Multi-GPU | No | Yes | Limited |
| Throughput (concurrent) | Low-Med | High | Low-Med |
| Single-user latency | Good | Good | Best |
| Memory efficiency | Good | Best | Good (quantization) |
| CPU inference | Limited | No | Excellent |
| AMD GPU support | Yes (ROCm) | Improving | Yes (ROCm/Vulkan) |
| Apple Silicon | Yes (Metal) | No | Yes (Metal) |
| OpenAI-compatible API | Yes | Yes | Yes |
| Model management | Built-in | External | External |
| Use Case | Model | Parameters | VRAM Required | Quality |
|---|---|---|---|---|
| General assistant | Llama 3.1 8B Instruct | 8B | 6-8 GB | Good |
| General assistant (better) | Llama 3.1 70B Instruct | 70B | 40-48 GB | Excellent |
| Code generation | DeepSeek Coder V2 | 16B/236B | 12-48 GB | Excellent |
| Code generation | Qwen 2.5 Coder 32B | 32B | 20-24 GB | Excellent |
| Document analysis | Mixtral 8x22B | 141B (sparse) | 48-80 GB | Excellent |
| Medical/Clinical | Med-PaLM (fine-tuned Llama) | Varies | Varies | Domain-specific |
| Summarization | Llama 3.1 8B + fine-tune | 8B | 6-8 GB | Good |
| Embedding/RAG | Nomic Embed Text | 137M | 1 GB | Excellent |
| Reasoning | QwQ 32B | 32B | 20-24 GB | Excellent |
Quantization reduces model size and VRAM requirements at a small quality cost:
| Quantization | Size Reduction | Quality Impact | VRAM Savings |
|---|---|---|---|
| FP16 (default) | 0% | None | Baseline |
| Q8 | ~50% | Negligible | ~50% |
| Q6_K | ~60% | Very minor | ~60% |
| Q5_K_M | ~65% | Minor | ~65% |
| Q4_K_M | ~75% | Noticeable for complex tasks | ~75% |
| Q3_K_M | ~80% | Moderate | ~80% |
Recommendation: Start with Q5_K_M or Q6_K for the best balance of quality and efficiency.
Best for small teams (5-20 users) with straightforward needs.
+-------------------+
| Single Server |
| - Ollama or vLLM |
| - 1x GPU |
| - Model storage |
| - API endpoint |
+-------------------+
Best for teams familiar with containers who want reproducible deployments.
# docker-compose.yml example
services:
ollama:
image: ollama/ollama
ports:
- "11434:11434"
volumes:
- ollama_data:/root/.ollama
deploy:
resources:
reservations:
devices:
- capabilities: [gpu]
restart: unless-stopped
open-webui:
image: ghcr.io/open-webui/open-webui:main
ports:
- "3000:8080"
environment:
- OLLAMA_BASE_URL=http://ollama:11434
volumes:
- webui_data:/app/backend/data
depends_on:
- ollama
restart: unless-stopped
volumes:
ollama_data:
webui_data:Best for enterprise scale with multiple GPU nodes and high availability requirements.
For classified or highly sensitive environments with no internet connectivity:
- Download models on an internet-connected system
- Transfer model files via approved media (encrypted USB, optical disc)
- Deploy inference framework from pre-built packages
- Validate model integrity via checksums
- Monitor via internal-only dashboards
See guides/security-hardening.md for the complete security hardening guide.
- Network isolation: Deploy AI infrastructure on a dedicated VLAN with strict firewall rules
- Authentication: Require authentication for all API access (API keys, OAuth 2.0, or mTLS)
- Authorization: Implement RBAC to control which users can access which models and data
- Encryption: TLS for all API communication, encryption at rest for model storage and logs
- Audit logging: Log all prompts and responses (with PII redaction where required)
- Input validation: Sanitize and validate all inputs to prevent prompt injection attacks
- Output filtering: Implement guardrails to prevent the model from generating harmful content
- Model integrity: Verify model checksums and maintain a software bill of materials (SBOM)
| Framework | Key AI-Related Requirements | How Private AI Helps |
|---|---|---|
| HIPAA | PHI must be on authorized systems, BAA required for cloud services | PHI never leaves your infrastructure |
| CMMC | CUI must be on assessed systems within the boundary | AI processing stays within CMMC boundary |
| SOC 2 | Data processing controls, vendor management | No third-party data processor for AI |
| ITAR | Technical data cannot leave US/authorized persons | Air-gapped deployment ensures compliance |
| GDPR | Data processing agreements, data residency | Full control over data location and retention |
| PCI DSS | Cardholder data protection | Payment data processed locally |
Every organization deploying AI should establish a governance framework. See templates/ai-governance-policy.md for a complete policy template covering:
- Acceptable use policies for AI tools
- Data classification and handling rules
- Model evaluation and approval processes
- Bias monitoring and fairness requirements
- Incident response for AI-specific failures
- Training requirements for AI users
| Metric | Target | Tool |
|---|---|---|
| Tokens/second (generation) | 30-80 tok/s per user | Framework metrics |
| Time to first token (TTFT) | < 500ms | API latency monitoring |
| GPU utilization | 70-90% during peak | nvidia-smi, Prometheus |
| VRAM usage | < 90% of capacity | nvidia-smi |
| Request queue depth | < 10 requests | Framework metrics |
| Error rate | < 0.1% | API monitoring |
- KV-cache optimization: Use PagedAttention (vLLM) to maximize concurrent requests
- Quantization: Use Q5_K_M or Q6_K for 60-65% memory savings with minimal quality loss
- Speculative decoding: Use a small draft model to accelerate generation from larger models
- Flash Attention: Enable FlashAttention 2 for significant memory and speed improvements
- Continuous batching: vLLM and TGI support batching multiple requests for higher throughput
- Model sharding: Distribute large models across multiple GPUs with tensor parallelism
Scenario: 50 users, ~100,000 tokens/user/day, Llama 3.1 70B equivalent quality
| Cost Factor | Cloud AI (GPT-4o) | Private AI (On-Prem) |
|---|---|---|
| Monthly API/inference cost | $15,000-$30,000 | $200 (electricity) |
| Hardware (amortized/mo over 3yr) | $0 | $1,400 |
| Staff time | Minimal | 10 hrs/mo ($2,000) |
| Total monthly cost | $15,000-$30,000 | $3,600 |
| Annual cost | $180,000-$360,000 | $43,200 |
| 3-year total | $540,000-$1,080,000 | $129,600 + $50,000 hardware |
Break-even: Private AI typically breaks even within 3-6 months for organizations with moderate usage.
- Define use cases and success criteria
- Assess existing infrastructure
- Deploy single-server Ollama with a 7-8B model
- Pilot with 5-10 users from a single team
- Measure quality, latency, and user satisfaction
- Select production hardware based on POC findings
- Deploy production inference framework (vLLM recommended)
- Implement security controls (authentication, encryption, logging)
- Deploy web UI for non-technical users (Open WebUI)
- Expand to 20-50 users across multiple teams
- Implement RAG pipeline for domain-specific knowledge
- Scale infrastructure for full user base
- Implement AI governance policy
- Deploy monitoring and alerting
- Integrate with existing workflows (API connections)
- Train users and administrators
- Establish ongoing operations procedures
- Fine-tune models on organization-specific data
- Evaluate new model releases for quality improvements
- Optimize hardware utilization and costs
- Expand use cases based on user feedback
- Conduct regular security assessments
| Resource | Purpose | Location |
|---|---|---|
| Hardware Selection Guide | Detailed GPU and system recommendations | guides/hardware-selection.md |
| Model Comparison | Ollama vs vLLM vs llama.cpp deep dive | guides/model-comparison.md |
| Security Hardening Guide | Complete security checklist for AI deployments | guides/security-hardening.md |
| AI Governance Policy | Template policy for organizational AI use | templates/ai-governance-policy.md |
| GPU Benchmark Script | Automated GPU performance testing | scripts/gpu-benchmark.sh |
Petronella Technology Group has been providing cybersecurity and IT infrastructure services for over 23 years. Founded by Craig Petronella, a 15x published author, PTG helps organizations deploy private AI solutions that meet strict compliance requirements while delivering real business value.
- AI readiness assessments -- Evaluate your infrastructure and use cases
- Hardware specification and procurement -- Right-sized GPU infrastructure
- Deployment and configuration -- Production-ready private AI environments
- Security hardening -- Compliance-aligned AI security controls
- RAG pipeline development -- Domain-specific knowledge integration
- Ongoing management -- Monitoring, updates, and optimization
- AI governance program development -- Policies, training, and oversight
- Website: petronellatech.com/ai/
- Phone: 919-348-4912
- Email: info@petronellatech.com
- Book: How to Avoid a Data Breach by Craig Petronella
- Podcast: Encrypted Ambition -- AI, cybersecurity, and business technology
- Free Consultation: Schedule a Call
We welcome contributions from the AI and security communities. Submit pull requests with hardware benchmarks, deployment guides, or security recommendations.
This project is licensed under the MIT License -- see the LICENSE file for details.
Built with hands-on private AI deployment experience by Petronella Technology Group -- Securing businesses for over 23 years.