AI Compute & Data Centers
More useful AI capacity. Before more hardware.
Explore how unnecessary execution, resource constraints, and reusable computational knowledge can affect the amount of useful work existing infrastructure can accomplish.
01The Capacity Multiplier
Relieve the binding constraint. Watch the next one appear.
Modeled useful throughput
85
req/s = min(compute, memory, network)
Binding constraint: Memory
Memory binds. Additional compute would add no useful throughput.
Not observed production telemetry. Not a proven customer gain.
02Qualified reuse
Reuse is only valuable when it is eligible.
100 required useful outcomes. 200 baseline execution calls. Some prior results could be reused — if their conditions still hold.
Source revision and scope match; prior results qualify.
Execution calls
170
Avoided
30
Calls / outcome
1.7
Independent of the capacity model above. Do not combine into a savings figure.
The problem
Clusters are scaled by utilization percentages, yet useful throughput is bound by whichever of compute, memory or network binds first — and much execution repeats work already done.
Density mechanism
Density models the binding constraint across resources and qualifies when prior computation can be reused, so capacity decisions follow useful work rather than busy hardware.
- 01Binding-constraint modeling across compute, memory and network
- 02Qualified reuse with explicit eligibility conditions
- 03Capacity frontier mapping before procurement
- 04Verification-burden accounting
Proposed pilot KPIs
≥ 10% of calls
Avoidable execution identified
Measured in pilot
Binding-constraint forecast accuracy
< 1%
Reuse false-eligibility rate
Targets are proposed for pilot qualification, not demonstrated results.