Private AI
On-Prem LLM Platform
Designed and delivered a secure GPU-backed internal LLM platform for high-throughput enterprise workflows.
Context
An enterprise team needed internal AI capability without exposing confidential data to external SaaS AI endpoints.
Problem
The organization lacked a controlled model-serving platform with predictable latency, policy controls, and operational visibility.
Approach
We designed a private AI stack with containerized inference services, role-aware policy controls, observability, and workload tuning for sustained demand.
Architecture
- GPU inference tier with queue-based request handling
- API and UI services isolated by role-aware access controls
- Retrieval and caching layers for repeat query acceleration
- Unified telemetry for latency, GPU utilization, and policy events
Architecture Diagram Placeholder
Results and Metrics
- 30,000 secure on-prem queries per day at steady state
- P95 latency reduced by 38% after inference tuning
- 100% authenticated access with auditable request logs
Tools and Stack
NVIDIA GPU nodes, Docker service composition, vLLM inference, Open-WebUI interface, and Loki-based log pipelines.
Lessons Learned
Private AI programs succeed when reliability, governance, and operational ownership are treated as first-class requirements alongside model quality.