Deployment Playbooks
From Unboxing to Operational
Hardware arriving at your facility is only the beginning. Getting AI infrastructure operational — correctly configured, properly networked, and running your workloads — requires a clear deployment process. DVUN's deployment playbooks provide step-by-step guidance for the most common AI infrastructure deployment scenarios, so you can move from delivery to production without unnecessary delays or costly mistakes.
Single-Node GPU Server Deployment
- Pre-Deployment Checklist — Facility requirements, power verification, network readiness, and software prerequisites before your server arrives.
- Physical Installation — Rack mounting, power connection, network cabling, and initial hardware verification.
- OS & Driver Installation — Recommended OS configurations, NVIDIA/AMD driver installation, and CUDA setup for AI workloads.
- Framework Installation & Validation — Installing PyTorch, TensorFlow, or your inference framework and validating GPU access and performance.
- First Workload Checklist — Verifying your system is ready for production workloads before going live.
Multi-Node Training Cluster Deployment
- Network Fabric Setup — Switch configuration, NIC installation, and InfiniBand or RoCE network bring-up for multi-node clusters.
- Cluster Management Setup — SLURM or equivalent cluster management software installation and configuration.
- Distributed Training Validation — Running NCCL tests and distributed training benchmarks to validate cluster performance before production use.
- Shared Storage Integration — Mounting and configuring shared storage for multi-node training workloads.
Inference Node Deployment
- Inference Server Configuration — OS and driver setup optimized for inference workloads. Disabling unnecessary services, configuring GPU persistence mode.
- Inference Framework Setup — Installing and configuring vLLM, TensorRT-LLM, Triton, or your inference serving framework of choice.
- Model Loading & Validation — Loading your model, validating inference performance, and benchmarking latency and throughput.
- Production Readiness Checklist — Monitoring setup, alerting configuration, and operational runbook before going live.
Private Compute Node Deployment
- Network Integration — Integrating your private compute node with your existing network infrastructure and security policies.
- Access Control Setup — User management, SSH key configuration, and access control for multi-user environments.
- Monitoring & Alerting — Setting up GPU utilization monitoring, temperature alerting, and operational dashboards.
- Backup & Recovery — Data backup procedures and recovery planning for on-premise AI infrastructure.
Need Hands-On Deployment Support?
Our playbooks cover standard deployment scenarios. If your deployment has unique requirements or you want expert support on-site or remote, our Deployment Readiness service is available.