Machine Learning Workstation

This is a configurable platform. We tune the components to your specific ML workflow. The RTX 5090 AI Workstation is a fixed, optimized configuration. If you are unsure which fits, our specialists will advise. In practice, choose the fixed configuration when your work is clearly GPU-bound and you already know 32GB of VRAM suits your models; choose this one when the balance between CPU, memory, storage and GPU is genuinely open, or when part of your pipeline is heavy preprocessing rather than training.

Work from the largest model you expect to run, not the one you are running now. Weights take roughly two bytes per parameter at BF16 and roughly half a byte at 4-bit, and training adds optimiser state and gradients on top, which is why LoRA-style methods fit where full fine-tuning does not. For system memory, aim comfortably above twice your VRAM: data loading, augmentation and checkpointing all happen there, and an underspecified host is the most common reason a new machine feels slower than expected.

Yes. Share your target specification and we will source and configure to match, or suggest improvements where they add value. We will also tell you where we think the specification is unbalanced: a common one is a large GPU behind too little system memory or a slow disk, which produces a machine that looks strong on paper and idles in practice. You are free to take the recommendation or not; the point is that you see it before the order rather than after.

Yes. We can standardize a configuration and image across a fleet for team-wide consistency. That means one validated component list, one documented software image with pinned versions, and asset records per unit, so IT supports one configuration rather than a collection of similar ones. If the rollout is large or staged across sites, the Enterprise AI Workstation is specified for exactly that and is usually the better starting point.

Yes, assembled, imaged and tested under load, with the operating system, drivers, CUDA toolchain and your frameworks installed and versions documented. Nothing arrives as a parts list. The documented version set is what lets you reproduce the environment later or replicate it on another machine, which matters more than it seems the first time a result cannot be reproduced.

When several people need it at once, when a trained model has to be served with an availability commitment, or when a single training run no longer fits on one machine. Workstations are single-tenant tools; adding users to one produces queuing rather than throughput. If any of those apply, look at /products/gpu-servers, and we will help you weigh a server against two more workstations honestly rather than default to whichever is larger.