How to choose an AI server for inference
Choosing inference hardware is about matching the machine to the job, not buying the biggest GPU you can. Here is a practical checklist.
Start with the model, not the GPU
The model you want to serve sets the floor: its size and precision decide how much VRAM you need. Everything else is built around keeping that model fed and responsive.
The checklist
- GPU and VRAM: sized to your model and precision, with headroom for concurrency.
- GPU count: one GPU for a single model, multiple for throughput or larger models.
- CPU and RAM: enough to handle pre- and post-processing without starving the GPU.
- Storage: fast NVMe sized to your models, datasets and logs.
- Network: 1 to 100GbE, matched to your traffic and data movement.
- Access: IPMI and root, so you stay in control after handover.
Think about growth
Size for the next 12 to 24 months, not just today. A little headroom in VRAM, storage and network saves a painful migration later.
Not sure where to land? That is exactly what workload discovery is for, we translate your models and traffic into a concrete specification.
Buy for the workload, host it properly
The best hardware still underperforms in a bad environment. Power, cooling and a fast uplink in a real datacenter matter as much as the spec sheet, which is why we host what we build.
In short: Match GPU, VRAM, CPU, storage and network to your model and traffic, with headroom for growth. Request a custom quote