On-Premise LLM Deployment
Complete control over your AI infrastructure. Keep sensitive data in-house, meet compliance requirements, and potentially save 30-50% on costs for high-volume workloads.
Why On-Premise?
Data Sovereignty
Data never leaves your network. Essential for GDPR, HIPAA, financial regulations.
Security Control
Your firewall, your policies, your monitoring. No third-party access.
Cost Predictability
30-50% savings over 3 years for high-volume (>60% GPU utilization).
Full Customization
Fine-tune with proprietary data. Modify models without restrictions.
Hardware Requirements (2024-2025)
| Model Size | GPU Requirement | Monthly Cost (Est.) | Use Case |
|---|---|---|---|
| 7B-13B | 1x RTX 4090 / A100 40GB | $600 - $3,000 | Small teams, experimentation |
| 30B-70B | 4-8x A100 80GB / H100 | $15,000 - $40,000 | Production workloads |
| 70B+ (high concurrency) | 8-16x H100 80GB | $50,000 - $150,000+ | Enterprise-scale |
💡 GPU Pricing: A100 80GB ~$15,000 | H100 80GB ~$30,000-40,000 per unit
Infrastructure Stack
Compute
- • NVIDIA DGX systems or custom GPU servers
- • H100/A100 GPUs with NVLink
- • High-bandwidth interconnect (InfiniBand)
Storage
- • NVMe SSDs for model weights
- • High-speed NFS for shared access
- • 2-5TB per deployment (models + cache)
Software Stack
- • Kubernetes + NVIDIA GPU Operator
- • vLLM / TGI / Triton Inference Server
- • Prometheus + Grafana monitoring
Cooling & Power
- • 300-700W per GPU
- • Liquid cooling recommended for H100
- • Dedicated UPS and generators
Typical Architecture
┌─────────────────────────────────────────────────────────────────────┐ │ Enterprise Network │ │ ┌─────────────┐ ┌─────────────────────┐ ┌───────────────┐ │ │ │ Users │────▶│ API Gateway │────▶│ Load Balancer│ │ │ │ (Internal) │ │ (Kong/NGINX) │ │ │ │ │ └─────────────┘ └─────────────────────┘ └───────┬───────┘ │ │ │ │ │ ┌───────────────────────────────────────────────────────▼───────┐ │ │ │ Kubernetes Cluster │ │ │ │ ┌─────────────────────────────────────────────────────────┐ │ │ │ │ │ vLLM / TGI Replicas (GPU Pods) │ │ │ │ │ │ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ │ │ │ │ │ │ Pod 1 (2xH100)│ │ Pod 2 (2xH100)│ │ Pod N (2xH100)│ │ │ │ │ │ │ │ Llama 70B │ │ Llama 70B │ │ Llama 70B │ │ │ │ │ │ │ └──────────────┘ └──────────────┘ └──────────────┘ │ │ │ │ │ └─────────────────────────────────────────────────────────┘ │ │ │ │ │ │ │ │ ┌──────────────┐ ┌──────────────┐ ┌───────────────────┐ │ │ │ │ │ Model Storage│ │ Vector DB │ │ Monitoring │ │ │ │ │ │ (NFS/S3) │ │ (Qdrant) │ │ (Prometheus) │ │ │ │ │ └──────────────┘ └──────────────┘ └───────────────────┘ │ │ │ └────────────────────────────────────────────────────────────────┘ │ └─────────────────────────────────────────────────────────────────────┘
Recommended Open Models
| Model | License | Strengths |
|---|---|---|
| Llama 3.1 70B | Llama Community | Best overall open model |
| Mixtral 8x22B | Apache 2.0 | MoE, cost efficient |
| Qwen2.5 72B | Apache 2.0 | Multilingual, coding |
| DeepSeek-R1 | MIT | Reasoning, math |
| CodeLlama 70B | Llama Community | Code generation |
On-Premise vs Cloud: Decision Guide
✓ Choose On-Premise When
- • GPU utilization consistently >60-70%
- • Strict data residency requirements
- • You have in-house ML/Ops expertise
- • Predictable, high-volume workloads
- • 3+ year investment horizon
☁️ Choose Cloud When
- • Variable or unpredictable demand
- • Need to scale quickly
- • Limited DevOps/ML resources
- • Experimentation phase
- • Need latest models immediately
🔀 Hybrid Approach (Most Common)
Many enterprises use on-premise for sensitive/high-volume workloads and cloud for burst capacity and experimentation.