Meta’s Llama model family has transformed the enterprise AI landscape. Open weights, permissive licensing, and competitive performance have made locally hosted large language models practical for organizations that previously had no alternative to cloud AI services.
This guide covers the practical considerations for enterprise deployment.
Model Selection
Llama 3 is available in multiple sizes. The 8B parameter model runs on a single consumer GPU and handles most text tasks competently. The 70B model requires multi-GPU configuration but approaches frontier model performance on many benchmarks. The choice depends on your task requirements, latency tolerance, and hardware budget.
For most enterprise use cases, starting with the 8B model and fine-tuning on domain data produces better results than running the 70B model out of the box. Domain-specific performance trumps general capability for production workloads.
Infrastructure Requirements
The minimum viable deployment for the 8B model is a single NVIDIA GPU with 16GB VRAM. For production deployment with reasonable throughput, a server with 2-4 GPUs provides headroom for concurrent requests and batch processing. The 70B model requires approximately 140GB of GPU memory, typically deployed across 2-4 high-memory GPUs.
Inference engines like vLLM, text-generation-inference, and Ollama simplify deployment. vLLM’s PagedAttention provides near-optimal GPU memory utilization. Ollama provides the simplest path from download to inference for teams new to local model deployment.
Fine-Tuning for Your Domain
Generic models produce generic outputs. Fine-tuning on your organization’s documents, terminology, and decision patterns creates a model that understands your domain at a level no cloud service can match without access to the same data.
LoRA (Low-Rank Adaptation) enables fine-tuning on a single GPU in hours rather than days. The resulting adapter can be swapped between base models, enabling rapid experimentation with minimal infrastructure cost.
Governance Integration
Local deployment does not eliminate governance requirements. The EIAF’s transparency, bias testing, explainability, and monitoring requirements apply regardless of where the model runs. The advantage of local deployment is that you control the entire governance stack. Monitoring, logging, access controls, and audit trails operate within your infrastructure and your policies.
The path from proof of concept to production is shorter than most organizations expect. The technology is ready. The governance framework is defined. The remaining variable is organizational will.