privacy
**Privacy and on-premise LLMs** refer to **deploying AI models within private infrastructure to maintain data sovereignty and compliance** — running LLMs on local servers, air-gapped environments, or private cloud without sending data to external APIs, essential for organizations with strict security, regulatory, or confidentiality requirements.
**What Are On-Premise LLMs?**
- **Definition**: LLMs deployed on organization-owned or controlled infrastructure.
- **Variants**: Self-hosted servers, private cloud, air-gapped systems.
- **Contrast**: External APIs where data leaves organizational control.
- **Models**: Open-weight models (Llama, Mistral, Qwen) deployable locally.
**Why On-Premise Matters**
- **Data Sovereignty**: Data never leaves your control.
- **Regulatory Compliance**: Meet HIPAA, GDPR, SOC2, ITAR requirements.
- **Confidentiality**: Trade secrets, legal, financial data stay internal.
- **Air-Gap**: Systems with no external network access.
- **Audit Trail**: Full control over logging and monitoring.
- **Cost Predictability**: Fixed GPU costs vs. variable API costs.
**Compliance Requirements**
```
Regulation | Key Requirements | On-Prem Benefits
---------------|----------------------------|------------------
HIPAA (Health) | PHI protection, access log | No external PHI
GDPR (EU) | Data residency, erasure | EU-located servers
SOC 2 | Access controls, audit | Full audit logs
ITAR (Defense) | US-only data processing | Controlled location
PCI-DSS | Cardholder data protection | Isolated network
CCPA | Consumer privacy rights | No third-party share
```
**Deployment Options**
**Self-Hosted Servers**:
- Own or lease GPU servers in your data center.
- Full control, highest responsibility.
- Examples: NVIDIA DGX, custom GPU servers.
**Private Cloud**:
- Dedicated instances in cloud provider.
- AWS VPC, Azure Private Link, GCP VPC.
- Some external dependency, more managed.
**Air-Gapped Systems**:
- No external network connectivity.
- Fully isolated from internet.
- Highest security, complex to maintain.
**Hardware Requirements**
```
Model Size | GPU Memory | Example Hardware
-----------|---------------|---------------------------
7B (FP16) | 14 GB | RTX 4090, single A100
7B (INT4) | 4 GB | RTX 3080, laptop GPU
13B (FP16) | 26 GB | A100-40GB, H100
70B (FP16) | 140 GB | 2× A100-80GB, 2× H100
70B (INT4) | 35 GB | A100-80GB, H100
405B | ~800 GB | 8× H100 or specialized
```
**On-Premise Serving Stack**
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**Security Considerations**
**Input Security**:
- Prompt injection protection.
- Input sanitization.
- Access control per user/role.
**Output Security**:
- PII detection and filtering.
- Content policy enforcement.
- Output logging for audit.
**Model Security**:
- Encrypted model storage.
- Access controls on weights.
- Prevent model extraction.
**API vs. On-Premise Trade-offs**
```
Factor | External API | On-Premise
---------------|--------------------|-----------------------
Data Privacy | Data leaves org | Data stays internal
Setup Effort | Minutes | Days to weeks
Maintenance | Provider handles | Your team handles
Latency | Network dependent | Local network only
Cost Model | Per-token usage | Fixed infrastructure
Updates | Automatic | Manual
```
**When to Choose On-Premise**
- Regulated industries (healthcare, finance, government).
- Sensitive data processing (legal, HR, M&A).
- High volume (>1M tokens/day — cost-effective).
- Air-gapped requirements (defense, critical infrastructure).
- Custom model requirements (fine-tuned proprietary models).
On-premise LLMs are **essential for organizations where data confidentiality is paramount** — enabling the benefits of AI while maintaining the security, compliance, and control that many industries require, making private deployment a critical capability in enterprise AI.