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The AI development stage is where ideas become working models. Engineers fine-tune open-weight models, test inference performance, build prototypes, and validate that local AI infrastructure delivers value before committing to production deployment. Getting the development stage hardware right — particularly GPU VRAM — determines iteration speed and the ceiling of what the team can prototype before hitting hardware constraints.
Why workstations, not servers, at the develop stage
In the development stage, individual engineers work directly on their own hardware interactively — pulling model weights, running fine-tuning jobs, evaluating outputs, iterating rapidly. A workstation on the engineer’s desk, running a desktop OS, connecting to their monitor, is the right form factor. A server in a rack room accessed over SSH introduces friction that slows interactive development work.
When the team is ready to move from experimentation to serving production users, that’s when the deploy stage GPU server becomes the right investment. Use the VRLA Tech AI ROI Calculator to calculate break-even between cloud API costs and on-premise hardware.
GPU VRAM sizing for development
- 7B–13B development: RTX 5090 (32GB) — comfortable FP16 with headroom for fine-tuning
- 34B development: RTX 5090 (32GB) — QLoRA fine-tuning fits comfortably
- 70B development: RTX PRO 6000 Blackwell (96GB) — FP8 fits on a single GPU with KV cache headroom
The development software stack
VRLA Tech validates the following on every AI development workstation before shipping: CUDA toolkit matched to target PyTorch release, PyTorch with CUDA confirmed, Hugging Face Transformers and PEFT, vLLM for local inference testing, Ollama for model management, Docker with NVIDIA Container Toolkit, and Conda for environment isolation.
Storage for active model development
A dedicated 4TB data NVMe separate from the OS drive holds model weights, datasets, and checkpoints. Fast NVMe PCIe 4.0 storage prevents storage from being the bottleneck when loading large models or writing fine-tuning checkpoints between experiments.
The path forward
Development workstations are stage one. As models are validated and team size grows, the progression is to a shared deploy-stage GPU server, then to scaled multi-server infrastructure. For teams that also need distributed training, VRLA Tech’s AI training cluster configurations extend the platform. See the full on-premise AI infrastructure roadmap.
Browse AI development stage hardware on the VRLA Tech AI Development Stage page.
Talk to a VRLA Tech engineer
Tell us your model targets, team size, and fine-tuning requirements. We configure the right development workstations and show you when a server investment makes sense.
AI development workstations. Pre-validated. Ships ready.
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.
