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The deploy stage is when AI stops being an internal experiment and starts being a production service. A model validated on a development workstation needs to be accessible to applications through a stable API endpoint that is always on, handles concurrent requests, and responds consistently. The infrastructure requirements for production deployment are substantially different from development — and getting them right from the start prevents painful migrations later.
What production deployment requires
Production AI deployment needs: always-on availability without an engineer actively running it, concurrent request handling for multiple simultaneous users, stable API endpoints at a fixed address, monitoring and alerting for throughput and errors, and enough VRAM for the model plus significant KV cache for concurrent requests.
The production serving stack
The standard production LLM serving stack is vLLM running as a persistent systemd service on a dedicated GPU server, exposing an OpenAI-compatible HTTP API. Applications connect to this endpoint by changing one configuration variable from the OpenAI API URL to your local server IP. The serving process runs continuously, restarts automatically on crash, and handles request queuing during demand spikes.
The deploy stage hardware
The VRLA Tech 4-GPU EPYC server with 384GB combined VRAM is the standard deploy-stage platform for most organizations. It runs LLaMA 3 70B at FP16 with 244GB of KV cache headroom, serves 100–200 concurrent users at typical context lengths, includes IPMI/BMC for headless remote management, dual redundant PSUs, and front-to-back airflow for sustained 24/7 operation without thermal throttling.
The ROI case for production infrastructure
Most teams reach deploy stage when their monthly cloud API spend consistently exceeds $2,000–$3,000. At that level, a VRLA Tech production server pays for itself within 4–8 months. Use the VRLA Tech AI ROI Calculator to run the exact numbers for your spend level.
The deploy stage checklist
- GPU server with sufficient VRAM for model plus concurrent KV cache
- vLLM or TensorRT-LLM installed and validated with target model
- Systemd service for automatic start and restart
- API endpoint accessible from client application network
- DCGM + Prometheus + Grafana monitoring configured
- Log aggregation for request tracking and debugging
Planning for the scale stage
Deploy-stage infrastructure should be designed with horizontal scaling in mind from day one — stateless API endpoints, model weights on shared storage, standardized server configurations that can be duplicated. When you are ready to grow, the VRLA Tech scale stage adds servers behind a load balancer without re-architecting. For teams with distributed training needs alongside production inference, see VRLA Tech AI training cluster configurations. For full data center deployments, see VRLA Tech data center deployment.
Browse deploy-stage GPU server configurations on the VRLA Tech AI Deploy Stage page and the VRLA Tech Server page.
Talk to a VRLA Tech engineer
Tell us your model, concurrent user count, uptime requirements, and current monthly AI spend. We configure the right server and show you the break-even date.
Production AI servers. vLLM pre-validated. Always-on API.
3-year parts warranty. Lifetime US engineer support.
VRLA Tech has been building custom AI workstations and GPU servers since 2016. Customers include General Dynamics, Los Alamos National Laboratory, and Johns Hopkins University. All systems ship with a 3-year parts warranty and lifetime US-based engineer support.
