Enterprise LLMs: A Case Study in Self-Hosting Llama 3

For many enterprises, sending sensitive legal or financial data to a public API is a non-starter. This case study details how we deployed a fine-tuned Llama 3 70B model on internal infrastructure to create a secure Legal Assistant.

The Challenge: Data Sovereignty

Our legal team needed an AI to summarize contracts, but our compliance policy strictly forbade sending client data to OpenAI or Anthropic.

• The Constraint: All inference must happen within our VPC.
• The Goal: GPT-4 level reasoning on legal texts.

The Solution: Self-Hosted Llama 3

We chose Meta’s Llama 3 70B for its balance of high reasoning capability and permissive license.

1. Infrastructure

We deployed the model on AWS SageMaker using g5.12xlarge instances (4x A10G GPUs).

• Engine: vLLM for high-throughput serving.

2. Fine-Tuning (QLoRA)

We didn’t just use the base model. We fine-tuned it on a dataset of 5,000 internal legal contracts using QLoRA (Quantized Low-Rank Adaptation).

• Cost: ~$400 in compute time.
• Result: The model learned our specific “Legal Dialect” and internal formatting standards.

Results & Impact

Conclusion

The era of “one model to rule them all” is ending. By self-hosting open weights models, enterprises can achieve better privacy, lower costs, and higher accuracy for domain-specific tasks.