Develop. Deploy. Scale.

The AI deployment pathway is VRLA Tech's three-stage framework for AI infrastructure: Develop (desk-side workstations for individuals), Deploy (shared GPU servers for teams), and Scale (data center LLM servers for production). Each stage maps to where a team is in its AI journey, from prototyping through production. Systems across all three stages ship with matching NVIDIA driver, CUDA, and framework versions so code and models move between stages without a rebuild.

Pick Develop if you're an individual researcher or small team of 1 to 3 people fine-tuning models at the desk. Pick Deploy if you're a team of 3 to 15 sharing a single multi-GPU machine for inference and fine-tuning. Pick Scale if you're running production inference, customer-facing AI, or training models at frontier scale requiring 24/7 data center operation. Many customers start at Develop and move up stages as their AI workload grows.

Yes. You don't have to progress linearly through the stages. Many customers go directly from Develop to Scale, skipping Deploy entirely if their workload jumps from individual prototyping to production deployment. The stages describe where teams commonly are, not a required sequence. Some enterprise customers start at Scale from day one.

VRLA Tech builds every system to your specific workload with no locked SKUs, typically delivers in 7 days to 6 weeks versus the 16 to 24 week OEM average, and includes lifetime US-based engineer support at no extra cost. You speak directly with the engineers who built your system — no tiered support contracts. Pricing usually runs 20 to 35 percent below equivalent Dell PowerEdge, HPE Cray, or Supermicro configurations without cutting component quality. Since 2016 we've served Fortune 500, federal agencies, and research labs including General Dynamics, Los Alamos National Laboratory, and Johns Hopkins.

Yes. Every VRLA Tech system across Develop, Deploy, and Scale ships with matching NVIDIA driver, CUDA, cuDNN, TensorRT, PyTorch, and framework versions. This is the primary advantage of running the full pathway on a single engineering team — a model developed on a Develop workstation deploys to a Scale server with no rebuild, and containers move up the pathway identically.

Yes. Many customers run a mix across stages — a few Develop workstations for individual researchers, a Deploy rackmount for team-shared workloads, and Scale servers for production inference. Because all three stages share the same software stack and engineering team, mixed deployments work out of the box with shared SSH access, shared model registries, and Slurm or Kubernetes scheduling where applicable.

Develop workstations start at $4,299.99 for a single-GPU Ryzen system and scale to about $12,000 for a dual-GPU Threadripper PRO. Deploy systems start at $11,649.99 for the Threadripper PRO tower and $13,899.99 for the 5U rackmount. Scale servers start at $13,949.99 for the 2U EPYC 4-GPU server and $35,999 for the 4U 8-GPU flagship. Multi-node cluster orders and frontier GPU configurations can exceed $100,000 per node.

Every VRLA Tech system at every stage 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, no call centers, no paid upgrades. For production-critical Scale deployments we also offer 4-hour and next-business-day on-site response SLAs in major US metros as an add-on.

VRLA Tech is based in Los Angeles, California. All systems are hand assembled and tested in our Los Angeles facility with a 48 to 96 hour burn-in process before shipping. Our engineering team is US-based and supports customers across the United States, Canada, and globally.