Private infrastructure
Dedicated infrastructure can support control over location and configuration. The owner or its operator must plan capacity, maintenance, physical security and recovery.
PLATFORM / FOUNDATIONS
The workload comes first.
A compute platform connects physical capacity, software and operational decisions. Explore the layers before choosing a deployment model.
WORKLOADSEnterprise applications · AI inference · Training · Simulation
PLATFORM & CONTROLVirtual machines · Containers · Scheduling · Identity · Observability
INFRASTRUCTURECPU / GPU · Memory · Storage · Networks
PHYSICAL FOUNDATIONPower · Cooling · Connectivity · Facilities
Conceptual architecture. Components and responsibilities vary by service.
DEPLOYMENT MODELS
Choose where. Understand who operates it.
Public cloud
Capacity is obtained as a service. Compare service scope, region, network charges, commitments and contract terms alongside hourly compute rates.
Hybrid deployment
A workload portfolio spans private and public environments. Consistent identity, network paths, recovery design and operating skills become central.
Edge deployment
Processing near users or equipment may address local response requirements. Remote operations, physical constraints and intermittent connections need explicit design.
CONTROL ACROSS THE LIFECYCLE
Ask better questions before committing.
Capacity & scheduling
Which jobs need accelerators? How much concurrent memory is required? Can batch work wait? Test with representative workloads; a node count is not a performance benchmark.
Data & operations
Who can access the data and control plane? Where are copies stored? Define identity, logging, encryption, backup and incident responsibilities.
Energy & resilience
Confirm measured IT draw, facility overhead and local power constraints. Document recovery objectives and test failure paths before assuming availability.
Background: [H2] [N2] [S2] [E1]. These planning questions are independent analysis.
Understand the technical trade-offs.
CPU, GPU and memory
CPUs suit general application logic and many data-processing tasks. GPUs can accelerate parallel work when the software supports them. Model size, memory capacity, data preparation and scheduling can constrain useful throughput.
Storage and networking
Capacity, latency, throughput and data movement must match the workload. More processors may sit idle if the data path cannot keep up. Test the complete workflow rather than comparing processor specifications alone.
Orchestration and operations
Virtual machines and containers package applications differently. Schedulers allocate resources; identity and policy define who may use them. Observability provides signals for investigation, while recovery requires separate tested procedures.
Power and cooling
Facility energy exceeds IT equipment energy because cooling and other systems consume power. The planner uses a user-supplied PUE multiplier, with no claim about a specific facility.