On-Premise LLM Deployment: Llama 3 vs Mistral

For enterprises handling sensitive data—financial records, healthcare information, proprietary code—sending prompts to external APIs is often not an option. The solution: run the LLM on your own infrastructure. But which model, and how?

Why On-Premise?

Before diving into benchmarks, let's be clear about when on-premise deployment makes sense:

• Regulatory requirements - GDPR, HIPAA, or industry-specific rules prohibit external data transfer
• Competitive sensitivity - Your data is your moat (e.g., proprietary code, trading strategies)
• Latency requirements - Sub-100ms response times for real-time applications
• Cost at scale - High-volume inference is cheaper on-premise after initial investment

If none of these apply, cloud APIs (OpenAI, Anthropic with EU data residency) are simpler and often sufficient.

The Contenders

We evaluated the two leading open-weight models for enterprise deployment:

Hardware Requirements

Running these models requires serious GPU power. Here's what we tested:

Performance Benchmarks

We ran benchmarks on identical hardware (4x A100 80GB, NVLink) using vLLM for inference optimization:

Quality Comparison

For our engineering use cases (document analysis, code review, incident triage), we evaluated on task-specific benchmarks:

Our Recommendation

Deployment Architecture

Here's our recommended production stack for on-premise LLM deployment:

services:
  vllm:
    image: vllm/vllm-openai:latest
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 4
              capabilities: [gpu]
    environment:
      - MODEL=meta-llama/Llama-3.1-70B-Instruct
      - QUANTIZATION=awq
      - MAX_MODEL_LEN=32768
    ports:
      - "8000:8000"

  vector-db:
    image: qdrant/qdrant:latest
    volumes:
      - qdrant_data:/qdrant/storage

  agent-orchestrator:
    image: aixagent/orchestrator:latest
    depends_on:
      - vllm
      - vector-db
    environment:
      - LLM_ENDPOINT=http://vllm:8000/v1
      - VECTOR_DB=http://vector-db:6333

Security Considerations

1. Network isolation

   Run LLM inference in a private subnet with no internet egress. All communication via internal load balancer.

2. Audit logging

   Log all prompts and responses (encrypted at rest) for compliance and debugging.

3. Input sanitization

   Filter prompts for prompt injection attempts before they reach the model.

4. Access control

   Use API keys and rate limiting. Consider per-team token budgets.

Conclusion

On-premise LLM deployment is now practical for enterprises willing to invest in GPU infrastructure. For most use cases, Llama 3.1 70B with INT8 quantization offers the best balance of performance, quality, and hardware efficiency.

The key is matching your deployment choice to your actual requirements. Not everyone needs the largest model—and not everyone needs on-premise at all. Start with your data sensitivity and latency requirements, then work backward to the right architecture.