Average target
—
transactions per second
8 billion transactions / day
Turn scale
into units.
A daily target sounds enormous until it becomes a per-second workload. Model the peak, find the headroom, then see what batching does to machine-payment fees.
Waiting for a scenario
Run the source sample to populate the capacity and fee evidence.
Peak demand against modeled capacity
not calculated
average —capacity bandpeak —
Peak demand
—
at selected multiplier
Capacity headroom
—
TPS after peak demand
Modeled utilization
—
peak / capacity
Micropayment events
—
per day at selected share
Batched transactions
—
per day at selected batch
Direct fee spend / day
—
Batched fee spend / day
—
Modeled savings / day
—
The completed model will explain whether peak demand fits and what batching changes.
Interpretation boundary
The source's 8B/day statement is preserved as a target, not treated as a benchmark. Capacity, fees, confirmation, batching, and post-quantum readiness need authoritative network evidence outside this local model.
The source's 8B/day statement is preserved as a target, not treated as a benchmark. Capacity, fees, confirmation, batching, and post-quantum readiness need authoritative network evidence outside this local model.
Daily volume hides a rate
Divide by 86,400 seconds before talking about scale. A peak multiplier turns the average into the workload your capacity must actually absorb.
Batching changes the bill
When many micropayment events share one transaction, modeled fee spend falls with submitted transaction count. The tradeoff is application-specific latency and trust, not magic zero fees.