Ready to Run
Hardware, drivers, and common compute tooling are planned as one stack.
Choose systems by workload first.Then match the hardware path to the software and performance target.
AI / ML Workstations
GPU workstations for local research, fine-tuning, inference, and day-to-day model development without starting in a shared cluster.
Hardware, drivers, and common compute tooling are planned as one stack.
Use a prepared environment for CUDA, containers, notebooks, and model tools.
Keep CPU, GPU, memory, storage, and cooling decisions aligned with the workload.
Choose air or water cooling according to density, noise, and duty cycle.
Support academic, startup, and production teams with repeatable configurations.
Plan around RTX, RTX PRO, H100, H200, B200, and B300 class parts.

Compact tower for one or two RTX GPUs and desk-side AI work.
Threadripper Pro platform for training, inference, and data work.

Maximum desk-side GPU density with enterprise water cooling.

Intel tower option for compact inference and research workloads.
Xeon W platform for multi-GPU model development and analysis.

High-end Intel platform for dense GPU training and simulation.

Rack format workstation for labs that need serviceable density.

Rackmount Xeon W system for shared lab and datacenter rooms.

External accelerator path for extending workstation VRAM.