Not every GPU workload needs a dedicated server, but there is a point where shared acceleration starts slowing the team down.
When shared GPU is still fine
- Short experiments and test environments
- Small inference workloads
- Low sensitivity to execution time
When dedicated GPU is the better call
- Long model training jobs
- Rendering and video processing with strict deadlines
- High demand for local NVMe and predictable CPU resources
What to evaluate
- VRAM size
- GPU type and count
- CPU, RAM and NVMe around the accelerator
- Network and access policy
A dedicated GPU server pays off when the team is buying predictable completion time, not just hardware.