Research labs
Compute that keeps up with the research
High VRAM, thermal stability under long runs, a clean CUDA and Docker stack, and headroom to add GPUs as the work grows.
Typical configurations
| Workload | GPU | VRAM | Memory |
|---|---|---|---|
| Training / fine-tuning neural nets, transformers | multi-GPU RTX 6000-class | 48 GB+ | 128-512 GB |
| Large-dataset inference batch, multi-user | RTX 5090 | 24 GB | 128 GB |
| Experimentation mixed workloads | RTX 5090 / 4090 | 24 GB | 64-128 GB |
Specced to your training frameworks and dataset scale.
01
VRAM with headroom
Sized so you are not re-buying in six months as models grow.
02
Sustained-load stable
Power, airflow, and drivers tuned for long training runs.
03
Scales up
Add GPUs or mesh a second node as the lab grows.