AI & HPC Infrastructure
Accelerated computing, planned as a system
AI training, inference and research computing need more than servers. Compute, network, storage, power and cooling have to be sized together, or the most expensive part of the cluster ends up waiting on the cheapest.
Accelerated compute
GPU-accelerated servers in air- and liquid-cooled configurations, from single inference nodes to multi-rack training clusters.
High-speed fabrics
Low-latency compute networks, separate storage and management networks, and the optics and cabling to connect them.
Storage
High-throughput parallel and object storage sized to keep accelerators fed with data.
Power & cooling
High-density rack power and air or direct-to-chip liquid cooling. See power & cooling engineering.
Common deployments
- Pilot & evaluation — a few nodes to prove out a workload.
- Enterprise inference — air-cooled systems that fit existing data halls.
- Research & universities — mixed AI and simulation clusters.
- Phased build-outs — capacity released in lots aligned to power-on dates.
What we need to recommend
- Workload type and model or dataset size
- Number of users or jobs and growth plans
- Target facility: power per rack, cooling type, space
- Timeline and budget range