On-Premise LLM Deployment: A Guide for Regulated Industries

For organizations in healthcare, legal, finance, and government, the promise of Generative AI is often tempered by a hard reality: data sovereignty. Sending sensitive client data, medical records, or intellectual property to a third-party API provider is often a non-starter due to GDPR, HIPAA, or strict internal security policies.

The solution is On-Premise LLM Deployment. By running models like Llama 3, Mistral, or Qwen on your own infrastructure, you maintain 100% control over your data. This guide provides a deep technical dive into the hardware, software, and security strategies required for a successful enterprise deployment.

Hardware Selection: The GPU Foundation

Choosing the right hardware is the most critical decision in an on-premise strategy. For enterprise-grade inference, NVIDIA GPUs are the industry standard due to the robust CUDA ecosystem.

GPU Comparison for Enterprise AI

| GPU Model | VRAM | Architecture | Primary Use Case | | :--- | :--- | :--- | :--- | | NVIDIA H100 | 80GB HBM3 | Hopper | Large-scale training and high-throughput production inference (70B+ models). | | NVIDIA A100 | 40/80GB | Ampere | The reliable workhorse for multi-user RAG and mid-sized model hosting. | | NVIDIA L40S | 48GB GDDR6 | Ada Lovelace | Optimized for fine-tuning and inference; excellent cost-to-performance ratio. | | RTX 6000 Ada | 48GB GDDR6 | Ada Lovelace | Ideal for high-end workstations and dedicated departmental servers. |

Sizing Your Inference Server

• •8B Models (e.g., Llama 3 8B): Can run on a single RTX 4090 or L4 instance (24GB VRAM) with 4-bit quantization.
• •70B Models (e.g., Llama 3 70B): Require at least 2x L40S or 2x A100 (80GB) to run at reasonable speeds.
• •405B Models: Require multi-node clusters with high-speed interconnects (NVLink/InfiniBand).

The Software Stack: Performance & Orchestration

Simply running a model isn't enough; enterprise deployments require high-speed serving engines to handle concurrent users.

Inference Engines

1. •vLLM: The current leader for high-throughput serving. It utilizes PagedAttention, which significantly reduces memory fragmentation and allows for much higher batch sizes.
2. •Ollama: Excellent for local development and departmental pilots. It provides a simple CLI and API for containerized LLM management.
3. •NVIDIA Triton Inference Server: Best for multi-model deployments (Vision, Speech, Text) in a unified pipeline.

Performance Benchmarks (Typical 70B Model)

• •Standard Transformers: ~5-10 tokens/sec
• •vLLM (Optimized): ~40-60 tokens/sec
• •Quantized (GGUF/AWQ): Up to 2x speedup with <1% accuracy loss.

Security & Networking Strategies

Deploying a model on-premise is only secure if the network architecture is sound.

1. Air-Gapped Deployments

For the highest security tier, servers are completely disconnected from the public internet. Model updates and weights are "sneakernetted" via secure drives after being scanned for vulnerabilities.

2. VPC & Network Isolation

The LLM inference cluster should reside in a dedicated VPC or VLAN. Access is restricted via:

• •mTLS: Mutual TLS for authenticated service-to-service communication.
• •Tailscale/Zero Tier: For secure, encrypted access from authorized employee devices without exposing the server to the open web.

3. Data Flow Governance

All prompts and completions should be logged to a local, immutable audit trail. This allows compliance officers to monitor for data leaks or "shadow AI" usage while keeping the logs entirely within the organization's perimeter.

Conclusion

On-premise LLM deployment is no longer a niche requirement—it is a strategic necessity for regulated industries. While the initial CapEx for hardware and the requirement for specialized DevOps skills are higher than using a Cloud API, the long-term benefits in security, fixed cost predictability, and data sovereignty are undeniable.

At Inlock AI, we specialize in architecting these private environments. If you are ready to move from a "Cloud-First" to a "Security-First" AI strategy, contact us to discuss your infrastructure requirements.