# Private LLM for Sensitive Data

## The Problem

Organizations with highly sensitive data cannot use cloud LLM APIs due to data governance policies, requiring fully private inference infrastructure.

### Symptoms

* ❌ Cloud APIs rejected by security
* ❌ Data cannot leave premises
* ❌ Need air-gapped deployment
* ❌ Compliance requires private models
* ❌ Cannot use OpenAI/Anthropic APIs

### Real-World Example

```
Defense contractor builds RAG:
→ Knowledge base: Classified documents
→ Cannot send queries to OpenAI (cloud)
→ Data residency: Must stay on-premise

Requirements:
→ Self-hosted LLM
→ No internet connectivity
→ Full data sovereignty
→ Comparable performance to GPT-4
```

***

## Deep Technical Analysis

### Cloud API Privacy Concerns

**Data Exposure:**

```
Cloud LLM APIs:
→ Query sent over internet to vendor
→ Retrieved context included in request
→ Potentially logged for training/monitoring
→ Third-party processors see data

Even with enterprise agreements:
→ Some organizations cannot accept risk
→ Regulatory requirements (ITAR, FedRAMP)
→ Must use private models
```

**Zero Data Retention Policies:**

```
Some vendors offer:
→ OpenAI: Zero retention (Enterprise)
→ Anthropic: No training on customer data

But still:
→ Data in transit through vendor systems
→ Temporary processing exposure
→ Not acceptable for highest security tiers
```

### Self-Hosted Model Options

**Open Source LLMs:**

```
Llama 2 (70B):
→ Quality ~GPT-3.5 level
→ Self-hostable
→ Free for commercial use

Mistral (7B/8x7B):
→ Strong performance
→ Efficient inference

Falcon (40B/180B):
→ Open weights
→ Competitive quality
```

**Infrastructure Requirements:**

```
Llama 2 70B:
→ 4x A100 GPUs (80GB each)
→ ~280GB VRAM total
→ Cost: ~$40K hardware
→ Or cloud GPU instances: $10-20/hour

For production:
→ Load balancing
→ Redundancy
→ Monitoring
→ DevOps overhead
```

**Quantization Trade-offs:**

```
Reduce model size:
→ 70B model at FP16: 140GB
→ 70B model at INT8: 70GB
→ 70B model at INT4: 35GB

Quality degradation:
→ FP16: 100% quality (baseline)
→ INT8: ~98% quality
→ INT4: ~92% quality

Fit on fewer GPUs but lower accuracy
```

### Embedding Model Privacy

**Self-Hosted Embeddings:**

```
sentence-transformers (open source):
→ all-MiniLM-L6-v2: Fast, good quality
→ all-mpnet-base-v2: Higher quality
→ CPU or GPU inference

Runs locally:
→ No API calls
→ No data leakage
→ Full control
```

**On-Device Embedding:**

```
For edge deployment:
→ ONNX runtime
→ Quantized models
→ Inference on CPU

Enables:
→ Fully offline RAG
→ No network dependency
```

### Air-Gapped Deployment

**Disconnected Environment:**

```
No internet access:
→ Download models beforehand
→ Transfer via physical media
→ Install in isolated network

Challenges:
→ Model updates: Manual process
→ No telemetry/monitoring (external)
→ Local logging only
```

**Supply Chain Security:**

```
Verify model provenance:
→ Check cryptographic signatures
→ Audit model weights (backdoors?)
→ Review training data sources

Open source advantage:
→ Weights inspectable
→ Community vetted
→ Reproducible builds
```

***

## How to Solve

**Deploy open-source LLMs (Llama 2 70B, Mistral) on-premise + use self-hosted embedding models (sentence-transformers) + implement quantization (INT8) to reduce hardware needs + set up air-gapped deployment for classified data + use vLLM or Text Generation Inference for efficient serving.** See [Private Models](/rag-scenarios-and-solutions/privacy/private-models.md).


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