Real-time services
Prioritize latency and availability. Keep required capacity, continuous supply and recovery capability.
EnerBoundless Infra ／ AI compute
For every kilowatt-hour,
how much useful compute is delivered?
AI compute planning has two questions: when power will be available, and how to increase useful output per kilowatt-hour while meeting quality, latency and reliability requirements.
Explore the energy calculatorWork back from service requirements.
Workload
Confirm model, throughput, latency, data location and billable service needs before choosing equipment and deployment.
Power and facilities
Separate IT energy from facility energy; verify rack density, cooling, network, grid connection and launch conditions.
Performance and cost
Test useful output and actual whole-system AC power under the same service conditions, alongside system and operating costs.
Turn efficiency gains
into an energy difference.
Set example conditions to see energy use for the same output. This is a teaching scenario, not a hardware performance promise.
Baseline facility power15 MW
Original daily energy360MWh / day
→Energy for the same output300MWh / day
Energy reduction for the same output 16.7%
If energy stays constant, the output index rises from 100 to 120.
Calculation: daily facility energy = IT load × PUE × 24; energy for the same output = baseline energy ÷ (1 + efficiency gain). Service quality and measurement boundaries are assumed equal; no second system-efficiency gain is added.
Flexibility depends on service commitments.
Batch workloads
Consider shifting work within delivery deadlines to align with generation, tariffs and equipment conditions.
Maintenance and backup
Plan transfer and maintenance windows; protect service with safety margins and fallback procedures.
Move from compute demand to a testable deployment.
For multi-site deployment and investment partnerships, verify equipment, facility rights, service contracts and operating responsibility before committing capital or expanding.
Verify usable capacity
Check facility space, power, cooling, leasing rights and launch dates; distinguish planned capacity from capacity available to lease.
Define services and revenue
Map dedicated compute, GPU hours and inference services to the same equipment time so output and revenue are not double counted.
Phase investment and governance
Validate demand and the first deployment, then expand based on contracts, utilization and service performance, with clear responsibility for data and operations.
ForwardAI × compute: a development direction.
Measure → recommend → authorized action
First attribute energy to sites, racks, servers and workloads, then simulate placement and schedules. Controlled action is considered only after site integration, SLA validation and permission checks.
AIDC and cross-site coordination are development directions; actual features and automation scope depend on each project's validation.
Let power conditions and service demand determine scale together.
Share workload, deployment timeline and site conditions. Begin with usable capacity and first-stage validation.
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