GPUs for AI/ML

GPU hosting in Georgia and the Southeast

Most GPU capacity in the United States sits in a handful of very large markets, which means teams in Georgia and the wider Southeast either ship data a long way or accept whatever latency the nearest hyperscale region offers. Our GPU capacity runs from our Alpharetta, GA facility, inside the Atlanta metro — close enough that training data staged on your own hardware does not cross the country to reach a GPU, and close enough to visit.

For teams already colocating in Atlanta, that proximity is the point: your storage and our GPUs can sit on the same fabric rather than talking over the public internet. See colocation and BYO hardware and our zones.

Dedicated GPU clusters, not shared slices

A GPU cluster solution is only useful if the cards are actually yours for the duration. We allocate dedicated GPUs with PCIe passthrough rather than time-slicing a card between tenants, so throughput does not move because someone else started a job. For multi-GPU training, interconnect and data-path bandwidth matter as much as the cards themselves — a cluster that starves its GPUs is an expensive way to wait.

Training and inference have different shapes

Training is throughput-bound and tolerant of latency: you want maximum sustained utilisation and enough storage bandwidth to keep the pipeline fed. Inference is the opposite — modest compute, but tail latency determines whether the product feels responsive. Sizing one deployment for both usually means overpaying for training capacity that sits idle, or under-serving inference at peak. We size them separately, and the low-latency path in front of inference is described under network fabric.

Cooling is the constraint nobody quotes

Modern accelerators are thermally limited long before they are compute limited, and a card that throttles is a card you are paying full price for at reduced output. Our facility runs both air and liquid cooling for GPU-dense deployments, which is what makes sustained training runs viable rather than merely possible.

Transparent pricing, no egress penalty

GPU hosting bills are frequently dominated by things that are not the GPU: data transfer to get training sets in, transfer to get checkpoints out, and storage charged at a premium tier. We do not charge egress fees, and block storage for datasets and checkpoints is priced on capacity — see storage architecture and pricing. If you are comparing against a hyperscaler quote, include the transfer line; it is often where the difference is.

Benchmark before you commit

The right way to choose GPU infrastructure is to run your own workload on it — not a synthetic benchmark, and not a spec sheet comparison. We will give you access to benchmark against your real training or inference job, on the configuration you would actually buy, before there is a contract.

Bring your own GPUs

If you already own accelerators, you do not have to rent ours to use our facility. Rack your cards with us and connect them to our storage and network under colocation, or run a mixed estate where your hardware and our cloud share one VPC — see hybrid cloud. Either way our engineers can operate it for you under managed services.

Ready to size a cluster? Talk to an engineer.