Cloud and On-Premises HPC
HPC runs both on dedicated on-premises clusters and in the cloud; each fits different needs for scale, latency, cost predictability, and control.
Two deployment models
Traditional HPC runs on on-premises clusters: dedicated hardware in an institution's data center, shared through a batch scheduler. Cloud HPC rents compute on demand from a provider, spinning resources up and down as needed. Many organizations now blend the two.
Where on-prem wins
- Sustained, high-utilization workloads run continuously
- Tightly coupled codes needing the lowest-latency interconnect
- Full control over hardware, data locality, and security
Where cloud wins
- Bursty or occasional demand, with no idle hardware to maintain
- Rapid access to the newest accelerators without procurement
- Elastic scaling for parameter sweeps and independent jobs
The coupling question
The technical dividing line is how tightly a workload is coupled. Embarrassingly parallel jobs, independent simulations or inference requests, run well anywhere. Tightly coupled jobs with frequent collectives are sensitive to interconnect quality; cloud providers now offer HPC-grade fabrics, narrowing but not erasing the gap with purpose-built machines.
Operational differences
On-prem shifts effort to procurement, facilities, power, and system administration, and its cost is largely fixed once the machine is built. Cloud shifts effort to orchestration, data transfer, and managing usage, and its resources appear and vanish on demand. Data gravity matters: moving large datasets in and out of the cloud is slow, so workloads often live where the data already is. Hybrid setups keep steady, well-characterized work on-prem and burst occasional peaks to the cloud, seeking the strengths of both.