Our On-Premise LLM Deployment Process
A structured approach to deploying large language models on your infrastructure, from initial assessment through production operations.
Infrastructure Assessment
We audit your existing hardware, network topology, and security requirements to design the optimal deployment architecture for your use cases and compliance needs.
Model Selection & Fine-Tuning
We evaluate open-source models against your requirements, benchmark performance on your hardware, and fine-tune with your domain data for maximum accuracy.
Containerized Deployment
We deploy models in Docker/Kubernetes containers with GPU orchestration, load balancing, and automated failover for production-grade reliability.
Security & Compliance Setup
We implement network isolation, encryption at rest and in transit, role-based access controls, and audit logging aligned with your compliance framework.
API Integration & Testing
We build REST and gRPC APIs for your applications to consume LLM capabilities, run load tests, and validate response quality before production launch.
Monitoring & Operations
We set up dashboards for inference metrics, GPU utilization, and model drift detection with 24/7 operational support and regular model updates.