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.