INFRASTRUCTURE
Understand the dependencies that turn a collection of accelerators into useful computing.
01 / COMPUTE & MEMORY
GPU memory capacity, numerical precision and application support affect what can run. CPU resources, host RAM and storage buffers also matter. A larger advertised accelerator count does not guarantee better application throughput.
02 / FABRIC & DATA MOVEMENT
Inside a distributed job, accelerator communication can become a limiting factor. Between jobs, dataset loading and checkpoints may dominate. Ask about interconnect topology, oversubscription, storage bandwidth and data-transfer charges.
03 / SCHEDULING & RESILIENCE
Schedulers assign work to resources. Kubernetes is often used for services and containers; Slurm is common in batch HPC. For either, establish checkpoint policy, failure handling, queue behaviour and monitoring responsibilities.
04 / ENERGY & OPERATIONS
Power usage effectiveness (PUE) is facility energy divided by IT energy over the same period. Our planner uses a user-entered non-GPU factor and PUE to illustrate an energy envelope. It is not metered energy, a carbon assessment or a cloud provider operating estimate.
05 / GOVERNANCE & PORTABILITY
Record approved regions, data classifications, access controls, audit requirements and the procedure for moving data and images out. Verify the provider’s actual terms, service scope and independent certifications where required.