Private LLM Deployment: A Guide for Enterprise IT Teams

The Case for Private LLMs

Every API call to a third-party LLM provider sends your enterprise data outside your security perimeter. For organizations in regulated industries - finance, healthcare, defense, legal - this is often a non-starter. Private LLM deployment puts the intelligence inside your walls.

Infrastructure Requirements

GPU Selection

For production LLM inference, NVIDIA H100 (80GB HBM3) is the gold standard, offering native FP8 support and 3.35 TB/s memory bandwidth. The A100 (80GB) remains a cost-effective alternative for teams not requiring bleeding-edge throughput. Budget at minimum 2x GPUs for redundancy.

Networking

Multi-GPU inference requires high-bandwidth interconnects. NVLink 4.0 (900 GB/s on H100) enables efficient tensor parallelism. For air-gapped deployments, ensure your network topology supports the latency requirements of your inference pipeline.

Storage

Model weights for a 70B parameter model consume approximately 140GB at FP16 or 70GB at FP8. Plan for NVMe SSDs with at least 2TB capacity per node to accommodate model checkpoints, calibration data, and logging.

Deployment Architecture

1. Model Server: Deploy using vLLM or TensorRT-LLM for optimized inference with PagedAttention and continuous batching
2. API Gateway: Place an authentication and rate-limiting layer in front of your model server
3. Monitoring: Implement comprehensive logging of all prompts, responses, latencies, and error rates
4. Load Balancer: For multi-replica deployments, distribute requests based on GPU utilization metrics

Security Best Practices

• Encrypt all data at rest and in transit within your inference pipeline
• Implement role-based access control for model endpoints
• Deploy input sanitization to prevent prompt injection attacks
• Maintain audit logs with full traceability from request to response
• Run regular penetration testing against your model API surface

Private LLM deployment is no longer a luxury reserved for tech giants. With mature open-source models and optimized inference frameworks, any enterprise with the will to invest can own their AI stack completely.