Deploy AI across your team.

The Deploy stage covers team-shared AI hardware — Threadripper PRO towers and 5U rackmount workstations sized for multiple users on a single machine. These systems bridge desk-side Develop workstations and full data center Scale deployments, letting teams run shared inference, fine-tuning, and production validation without committing to a dedicated server rack.

Choose a Threadripper PRO tower if your team is still in an office or small lab environment without a dedicated server room. Choose the 5U rackmount if you already have a server rack, want redundant cooling and professional cable management, or plan to scale to multiple units soon. The 5U chassis can also run as a standalone tower if you aren't ready to rack yet.

Both Deploy-stage systems support up to 4 NVIDIA RTX PRO Blackwell GPUs, giving you up to 384 GB of combined VRAM when configured with 4× RTX PRO 6000. You can also mix lower-tier Blackwell cards for budget-matched team configurations.

Threadripper PRO 9000 WX-series offers 8-channel DDR5 ECC memory, 128 PCIe 5.0 lanes, and strong single-threaded performance at a lower price point than dual-socket EPYC. For team-shared workstation use — where individual researcher workloads matter more than multi-tenant throughput — Threadripper PRO delivers the best balance of performance, cost, and workstation-class familiarity. EPYC dual-socket makes more sense at Scale, not Deploy.

Yes. Deploy-stage systems are configured for multi-user access via SSH, remote desktop, or containerized workloads. Common patterns include GPU partitioning with NVIDIA MIG, containerized development environments via Docker or Podman, and scheduled job queues via Slurm or Ray. We can pre-configure any of these at order time.

A fully configured 4-GPU Deploy system with Threadripper PRO and 4× RTX PRO 6000 draws 2,000 to 2,400 watts at full load and requires a dedicated 208V or two 120V 20A circuits. Heat output is approximately 7,500 to 8,500 BTU/hr. The 5U rackmount includes redundant cooling and hot-swap fans; the tower uses premium air cooling sized for 24/7 team use.

Yes. Every VRLA Tech system across Develop, Deploy, and Scale ships with matching NVIDIA driver, CUDA, cuDNN, TensorRT, PyTorch, and framework versions. Models and containers developed on a Develop workstation deploy to a Deploy system with no rebuild, and the same holds for moving from Deploy to Scale.

Yes. The 5U chassis is designed for dual use — rackmountable in a standard 19-inch server rack with optional rails, or usable as a standalone tower on the floor of a lab or office. Many customers start with it as a tower and rack it later once they build out server room infrastructure.

Standard Deploy-stage systems ship in 7 to 10 business days from order confirmation, which includes build, 48 to 72 hour burn-in testing, thermal validation, and packaging. Complex multi-GPU configurations or specialty components may add lead time — we give you a firm timeline upfront at order confirmation.

VRLA Tech builds every Deploy system to your exact workload with no locked SKUs, typically delivers in 7–10 business days versus the 16–24 week OEM average, and includes lifetime US engineer support at no extra cost. Our pricing usually runs 20–30% below equivalent Dell or HPE workstation-class server configurations without cutting component quality.

Every VRLA Tech Deploy-stage system includes a 3-year parts warranty and lifetime US-based engineer support at no extra cost. You speak directly with the engineers who built your system — no tiered support contracts, no call centers, no paid upgrades.

Yes. Many customers start at Deploy with a rackmount workstation and add Scale-stage servers as production workloads grow. We match driver, CUDA, and framework versions across every stage, and can spec the Deploy system with cluster expansion in mind — matching future node specs and reserving fabric capacity for InfiniBand or 400 GbE when you're ready.